Journal articles on the topic 'Observations Signal processing'
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Mudge, Todd D., and Rolf G. Lueck. "Digital Signal Processing to Enhance Oceanographic Observations." Journal of Atmospheric and Oceanic Technology 11, no. 3 (June 1994): 825–36. http://dx.doi.org/10.1175/1520-0426(1994)011<0825:dspteo>2.0.co;2.
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Padin, Stephen, and Schubert F. Soares. "Signal Processing Developments for the OVRO Array." International Astronomical Union Colloquium 140 (1994): 82–86. http://dx.doi.org/10.1017/s0252921100019187.
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Abstract An optical fiber IF transmission and tracking delay system and a wideband continuum correlator have been developed for the Owens Valley (OVRO) Millimeter Array. The IF system processes two 1-2 GHz bands which are frequency multiplexed through an optical fiber link. This allows simultaneous dual wavelength observations in the 2.7 and 1.3-mm bands or dual polarization observations in either band.
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Kumar, R. Suresh, and P. Manimegalai. "Detection and Separation of Eeg Artifacts Using Wavelet Transform." International Journal of Informatics and Communication Technology (IJ-ICT) 7, no. 3 (December 1, 2018): 149. http://dx.doi.org/10.11591/ijict.v7i3.pp149-156.
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Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
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Lucjan Setlak and Rafał Kowalik. "E1 Signal Processing of the Galileo System in the Navigation Receiver." Communications - Scientific letters of the University of Zilina 23, no. 3 (July 1, 2021): E46—E55. http://dx.doi.org/10.26552/com.c.2021.3.e46-e55.
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The subject of this article are issues related to the navigation system in the field of analyzing the processed signal in the GNSS system receiver. The main purpose of the work is to discuss the Galileo E1 signal processing methods in the GNSS navigation system receiver, supported by adapted research tools in terms of solving the research problem (analysis, model, simulation tests) and the mathematical apparatus used. Key studies are concentrated around the process of generating the navigation data, dispersing sequences and signal modulation. Thus, when designing a receiver, it is better to use the simulation signals than the real ones, since one can get more control over the properties of the received signal. In the final part of the work, in accordance with the subject of research, based on the developed appropriate research tools, observations and final conclusions were formulated, which have practical applications.
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Schroth, Arno, Karl Tragi, Ernst Lüneburg, and Madhukar Chandra. "Polarimetric signal processing of meteorological target observations with the DLR weather radar." European Transactions on Telecommunications 3, no. 4 (July 1992): 381–98. http://dx.doi.org/10.1002/ett.4460030411.
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Ogunfunmi, Tokunbo. "Adaptive Signal Processing and Machine Learning Using Entropy and Information Theory." Entropy 24, no. 10 (October 8, 2022): 1430. http://dx.doi.org/10.3390/e24101430.
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Möller, Gregor, and Daniel Landskron. "Atmospheric bending effects in GNSS tomography." Atmospheric Measurement Techniques 12, no. 1 (January 3, 2019): 23–34. http://dx.doi.org/10.5194/amt-12-23-2019.
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Abstract. In Global Navigation Satellite System (GNSS) tomography, precise information about the tropospheric water vapor distribution is derived from integral measurements like ground-based GNSS slant wet delays (SWDs). Therefore, the functional relation between observations and unknowns, i.e., the signal paths through the atmosphere, have to be accurately known for each station–satellite pair involved. For GNSS signals observed above a 15∘ elevation angle, the signal path is well approximated by a straight line. However, since electromagnetic waves are prone to atmospheric bending effects, this assumption is not sufficient anymore for lower elevation angles. Thus, in the following, a mixed 2-D piecewise linear ray-tracing approach is introduced and possible error sources in the reconstruction of the bended signal paths are analyzed in more detail. Especially if low elevation observations are considered, unmodeled bending effects can introduce a systematic error of up to 10–20 ppm, on average 1–2 ppm, into the tomography solution. Thereby, not only the ray-tracing method but also the quality of the a priori field can have a significant impact on the reconstructed signal paths, if not reduced by iterative processing. In order to keep the processing time within acceptable limits, a bending model is applied for the upper part of the neutral atmosphere. It helps to reduce the number of processing steps by up to 85 % without significant degradation in accuracy. Therefore, the developed mixed ray-tracing approach allows not only for the correct treatment of low elevation observations but is also fast and applicable for near-real-time applications.
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Yardim, Caglar, Peter Gerstoft, and Zoi-Heleni Michalopoulou. "Geophysical signal processing using sequential Bayesian techniques." GEOPHYSICS 78, no. 3 (May 1, 2013): V87—V100. http://dx.doi.org/10.1190/geo2012-0180.1.
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Sequential Bayesian techniques enable tracking of evolving geophysical parameters via sequential observations. They provide a formulation in which the geophysical parameters that characterize dynamic, nonstationary processes are continuously estimated as new data become available. This is done by using prediction from previous estimates of geophysical parameters, updates stemming from physical and statistical models that relate seismic measurements to the unknown geophysical parameters. In addition, these techniques provide the evolving uncertainty in the estimates in the form of posterior probability density functions. In addition to the particle filters (PFs), extended, unscented, and ensemble Kalman filters (EnKFs) were evaluated. The filters were compared via reflector and nonvolcanic tremor tracking examples. Because there are numerous geophysical problems in which the environmental model itself is not known or evolves with time, the concept of model selection and its filtering implementation were introduced. A multiple model PF was then used to track an unknown number of reflectors from seismic interferometry data. We found that when the equations that define the geophysical problem are strongly nonlinear, a PF was needed. The PF outperformed all Kalman filter variants, especially in low signal-to-noise ratio tremor cases. However, PFs are computationally expensive. The EnKF is most appropriate when the number of parameters is large. Because each technique is ideal under different conditions, they complement each other and provide a useful set of techniques for solving sequential geophysical inversion problems.
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Sirri, Paul, Elizabeth M. Palmer, and Essam Heggy. "Processing and Analysis for Radio Science Experiments (PARSE): Graphical Interface for Bistatic Radar." Planetary Science Journal 3, no. 1 (January 1, 2022): 24. http://dx.doi.org/10.3847/psj/ac3a07.
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Abstract Opportunistic bistatic radar (BSR) observations of planetary surfaces can probe the textural and electrical properties of several solar system bodies without needing a dedicated instrument or additional mission requirements, providing unique insights into volatile enrichment and supporting future landing, anchoring, and in situ sampling. Given their opportunistic nature, complex observation geometries, and required radiometric knowledge of the received radio signal, these data are particularly challenging to process, analyze, and interpret for most planetary science data users, who can be unfamiliar with link budget analysis of received echoes. The above impedes real-time use of BSR data to support mission operations, such as identifying safe landing locations on small bodies, as was the case for the Rosetta mission. To address this deficiency, we develop an open-source graphical user interface—Processing and Analysis for Radio Science Experiments (PARSE)—that assesses the feasibility of performing BSR observations and automates radiometric signal processing, power spectral analysis, and visualization of DSN planetary radio science data sets acquired during mission operations or archived on NASA’s Planetary Data System. In this first release, PARSE automates the processing chain developed for Dawn at Asteroid Vesta, streamlining the detection of DSN-received surface-scatter echoes generated as the spacecraft enters/exits occultations behind the target. Future releases will include support for existing Arecibo data sets and other Earth-based radio observatories. Our tool enables the broader planetary science community, beyond planetary radar signal processing experts, to utilize BSR data sets to characterize electrical and textural properties of planetary surfaces. Such tools are becoming increasingly important as the number of space missions—and subsequent opportunities for orbital radio science observations—continue to grow.
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Xu, Pengfei, Yinjie Jia, and Mingxin Jiang. "Blind audio source separation based on a new system model and the Savitzky-Golay filter." Journal of Electrical Engineering 72, no. 3 (June 1, 2021): 208–12. http://dx.doi.org/10.2478/jee-2021-0029.
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Abstract Blind source separation (BSS) is a research hotspot in the field of signal processing. This scheme is widely applied to separate a group of source signals from a given set of observations or mixed signals. In the present study, the Savitzky-Golay filter is applied to smooth the mixed signals, adopt a simplified cost function based on the signal to noise ratio (SNR) and obtain the demixing matrix accordingly. To this end, the generalized eigenvalue problem is solved without conventional iterative methods. It is founded that the proposed algorithm has a simple structure and can be easily implemented in diverse problems. The obtained results demonstrate the good performance of the proposed model for separating audio signals in cases with high signal to noise ratios.
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Price, D. C., L. Staveley-Smith, M. Bailes, E. Carretti, A. Jameson, M. E. Jones, W. van Straten, and S. W. Schediwy. "HIPSR: A Digital Signal Processor for the Parkes 21-cm Multibeam Receiver." Journal of Astronomical Instrumentation 05, no. 04 (December 2016): 1641007. http://dx.doi.org/10.1142/s2251171716410075.
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HIPSR (HI-Pulsar) is a digital signal processing system for the Parkes 21-cm Multibeam Receiver that provides larger instantaneous bandwidth, increased dynamic range, and more signal processing power than the previous systems in use at Parkes. The additional computational capacity enables finer spectral resolution in wideband HI observations and real-time detection of Fast Radio Bursts during pulsar surveys. HIPSR uses a heterogeneous architecture, consisting of FPGA-based signal processing boards connected via high-speed Ethernet to high performance compute nodes. Low-level signal processing is conducted on the FPGA-based boards, and more complex signal processing routines are conducted on the GPU-based compute nodes. The development of HIPSR was driven by two main science goals: to provide large bandwidth, high-resolution spectra suitable for 21-cm stacking and intensity mapping experiments; and to upgrade the Berkeley–Parkes–Swinburne Recorder (BPSR), the signal processing system used for the High Time Resolution Universe (HTRU) Survey and the Survey for Pulsars and Extragalactic Radio Bursts (SUPERB).
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Creutzig, Felix, Jan Benda, Sandra Wohlgemuth, Andreas Stumpner, Bernhard Ronacher, and Andreas V. M. Herz. "Timescale-Invariant Pattern Recognition by Feedforward Inhibition and Parallel Signal Processing." Neural Computation 22, no. 6 (June 2010): 1493–510. http://dx.doi.org/10.1162/neco.2010.05-09-1016.
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The timescale-invariant recognition of temporal stimulus sequences is vital for many species and poses a challenge for their sensory systems. Here we present a simple mechanistic model to address this computational task, based on recent observations in insects that use rhythmic acoustic communication signals for mate finding. In the model framework, feedforward inhibition leads to burst-like response patterns in one neuron of the circuit. Integrating these responses over a fixed time window by a readout neuron creates a timescale-invariant stimulus representation. Only two additional processing channels, each with a feature detector and a readout neuron, plus one final coincidence detector for all three parallel signal streams, are needed to account for the behavioral data. In contrast to previous solutions to the general time-warp problem, no time delay lines or sophisticated neural architectures are required. Our results suggest a new computational role for feedforward inhibition and underscore the power of parallel signal processing.
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Nguyen, Cuong M., and V. Chandrasekar. "Gaussian Model Adaptive Processing in Time Domain (GMAP-TD) for Weather Radars." Journal of Atmospheric and Oceanic Technology 30, no. 11 (November 1, 2013): 2571–84. http://dx.doi.org/10.1175/jtech-d-12-00215.1.
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Abstract The Gaussian model adaptive processing in the time domain (GMAP-TD) method for ground clutter suppression and signal spectral moment estimation for weather radars is presented. The technique transforms the clutter component of a weather radar return signal to noise. Additionally, an interpolation procedure has been developed to recover the portion of weather echoes that overlap clutter. It is shown that GMAP-TD improves the performance over the GMAP algorithm that operates in the frequency domain using both signal simulations and experimental observations. Furthermore, GMAP-TD can be directly extended for use with a staggered pulse repetition time (PRT) waveform. A detailed evaluation of GMAP-TD performance and comparison against the GMAP are done using simulated radar data and observations from the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar using uniform and staggered PRT waveform schemes.
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Zaitsev, Dmitry, Vitaliy Bryksin, Konstantin Belotelov, Yulia Kompaniets, and Roman Iakovlev. "Algorithms and Measuring Complex for Classification of Seismic Signal Sources, Determination of Distance and Azimuth to the Point of Excitation of Surface Waves." Informatics and Automation 21, no. 6 (November 24, 2022): 1211–39. http://dx.doi.org/10.15622/ia.21.6.5.
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Machine learning and digital signal processing methods are used in various industries, including in the analysis and classification of seismic signals from surface sources. The developed wave type analysis algorithm makes it possible to automatically identify and, accordingly, separate incoming seismic waves based on their characteristics. To distinguish the types of waves, a seismic measuring complex is used that determines the characteristics of the boundary waves of surface sources using special molecular electronic sensors of angular and linear oscillations. The results of the algorithm for processing data obtained by the method of seismic observations using spectral analysis based on the Morlet wavelet are presented. The paper also describes an algorithm for classifying signal sources, determining the distance and azimuth to the point of excitation of surface waves, considers the use of statistical characteristics and MFCC (Mel-frequency cepstral coefficients) parameters, as well as their joint application. At the same time, the following were used as statistical characteristics of the signal: variance, kurtosis coefficient, entropy and average value, and gradient boosting was chosen as a machine learning method; a machine learning method based on gradient boosting using statistical and MFCC parameters was used as a method for determining the distance to the signal source. The training was conducted on test data based on the selected special parameters of signals from sources of seismic excitation of surface waves. From a practical point of view, new methods of seismic observations and analysis of boundary waves make it possible to solve the problem of ensuring a dense arrangement of sensors in hard-to-reach places, eliminate the lack of knowledge in algorithms for processing data from seismic sensors of angular movements, classify and systematize sources, improve prediction accuracy, implement algorithms for locating and tracking sources. The aim of the work was to create algorithms for processing seismic data for classifying signal sources, determining the distance and azimuth to the point of excitation of surface waves.
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Aksoylar, Cem, George K. Atia, and Venkatesh Saligrama. "Sparse Signal Processing With Linear and Nonlinear Observations: A Unified Shannon-Theoretic Approach." IEEE Transactions on Information Theory 63, no. 2 (February 2017): 749–76. http://dx.doi.org/10.1109/tit.2016.2605122.
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Bello, N., and K. O. Ogbeide. "Spectral Noise Estimation: A Python 3 Implementation of the Minimum Statistics Estimation." March 2022 6, no. 1 (March 2022): 1–12. http://dx.doi.org/10.36263/nijest.2022.01.0300.
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Noise estimation has been used majorly in imaging processing and voice speech recognition applications. Therefore, researchers have found optimal solutions to non-stationary noise estimation. Particularly, there is a proposed method that estimates spectral noise in a noisy speech signal which is based on two observations; speech pauses and approximation of power spectral densities of the noisy signal to the true noise during speech pauses. Though from recent studies, the observations obtained cannot be inferred for other types of signals especially RF signals and have not been tested on signals in the frequency domain, this paper bridges that gap of research and presents the results, analysis, and conclusion on the findings concerning the noise estimation with RF signals using an extension of the proposed method in the frequency domain. It presents a detailed methodology of implementation of the minimum statistics method for noise estimation in python 3 code which was tested with RF signals and thus met the requirement of dynamic thresholding with spectrum occupancy measurement.
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Yu, Wang, Li, Chang, and Li. "Snow Depth Estimation with GNSS-R Dual Receiver Observation." Remote Sensing 11, no. 17 (September 1, 2019): 2056. http://dx.doi.org/10.3390/rs11172056.
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Two estimation methods using a dual GNSS (Global Navigation Satellite System) receiver system are proposed. The dual-frequency combination method combines the carrier phase observations of dual-frequency signals, whereas the single-frequency combination method combines the pseudorange and carrier phase observations of a single-frequency signal, both of which are geometry-free strictly combination and free of the effect of ionospheric delay. Theoretical models are established in the offline phase to describe the relationship between the spectral peak frequency of the combined sequence and the antenna height. A field experiment was conducted recently and the data processing results show that the root mean squared error (RMSE) of the dual-frequency combination method is 5.04 cm with GPS signals and 6.26 cm with BDS signals, which are slightly greater than the RMSE of 4.16 cm produced by the single-frequency combination method of L1 band with GPS signals. The results also demonstrate that the proposed two combination methods and the SNR method achieve similar performance. A dual receiver system enables the better use of GNSS signal carrier phase observations for snow depth estimation, achieving increased data utilization.
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Prochniewicz, Dominik, Jacek Kudrys, and Kamil Maciuk. "Noises in Double-Differenced GNSS Observations." Energies 15, no. 5 (February 23, 2022): 1668. http://dx.doi.org/10.3390/en15051668.
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Precise data processing from the Global Navigation Satellite Systems (GNSS) reference station network is mainly based on a combination of double-differenced carrier phase and code observations. This approach allows most of the measurement errors to be removed or reduced and is characterized as the most accurate method. However, creating observation differences between two receivers and two satellites increases the measurement noise of the observations by a factor of 2. As a result, it increases the impact of the incorrect definition of the noise characteristic on the results of the estimation of the unknowns in the positioning model. This is especially important in Multi-GNSS solutions, which integrate measurements from different systems, for which the stochastic parameters of observation may differ significantly. In this paper, the authors prepared a complex analysis of the noise type in double-differenced GNSS (GPS, GLONASS and Galileo) observations, both carrier phase and code ones, with a 1 s sampling interval. The Autocorrelation Function (ACF) method, the Lomb–Scargle (L-S) periodogram method, and the Allan variance (AVAR) method were used. The results that were obtained for the weekly set of measurement data showed that, depending on the system and type of observation, the noise level and its type are significantly different. Among the code measurements, the lowest noise levels were obtained for the GPS C5Q and Galileo C7Q/C8Q observations, with the standard deviations not exceeding ±10 cm, while the noisiest observations were for the GLONASS C1C and C2C signals, which had standard deviations of about ±90 cm and ±45 cm, respectively. For the carrier phase observations, each signal type was characterized by very similar noise levels of ±1.5–3.5 mm. The ACF analysis showed that 1 Hz double-differenced GNSS data can only be treated as being not correlated to time for carrier phase observations; for code observations, an irrelevant autocorrelation may be considered for measurement intervals greater than 20 s. Depending on the GNSS signals, the spectral index k varies in a range from −1.3 to −0.2 for code data and k = 0.0 in the case of phase data. Using the modified Allan deviation (MDEV) allows for specific noise types for each signal and GNSS system to be determined. All of the code observations were characterized by either flicker PM or white PM. In the case of the phase observations, they were all uniquely characterized by white PM (GPS and Galileo or by white PM and flicker PM (GLONASS).
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Yuan, Shihao, Andreino Simonelli, Chin-Jen Lin, Felix Bernauer, Stefanie Donner, Thomas Braun, Joachim Wassermann, and Heiner Igel. "Six Degree-of-Freedom Broadband Ground-Motion Observations with Portable Sensors: Validation, Local Earthquakes, and Signal Processing." Bulletin of the Seismological Society of America 110, no. 3 (May 12, 2020): 953–69. http://dx.doi.org/10.1785/0120190277.
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ABSTRACT The additional observation of three components of rotational ground motions has benefits for tilt-seismometer coupling (e.g., ocean-bottom seismometry and volcano seismology), local site characterization, wavefield separation, source inversion, glacial and planetary seismology, as well as the monitoring of structural health. Field applications have been mostly hampered by the lack of portable sensors with appropriate broadband operation range and weak-motion sensitivity. Here, we present field observations of the first commercial portable broadband rotation sensor specifically designed for seismology. The sensor is a three-component fiber-optic gyro strictly sensitive to ground rotation only. The sensor field performance and records are validated by comparing it with both array-derived rotation measurements and a navigation-type gyro. We present observations of the 2018 Mw 5.4 Hualien earthquake and the 2016 central Italy earthquake sequence. Processing collocated rotation and classical translation records shows the potential in retrieving wave propagation direction and local structural velocity from point measurements comparable to small-scale arrays of seismic stations. We consider the availability of a portable, broadband, high sensitivity, and low self-noise rotation sensor to be a milestone in seismic instrumentation. Complete and accurate ground-motion observations (assuming a rigid base plate) are possible in the near, local, or regional field, opening up a wide range of seismological applications.
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Wilczynski, V., and M. J. Casarella. "Influences of Near-Wall and Induced Irrotational Motion in a Turbulent Boundary Layer on Wall Pressure Fluctuations." Journal of Energy Resources Technology 117, no. 4 (December 1, 1995): 252–62. http://dx.doi.org/10.1115/1.2835421.
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While many studies have investigated the influence of flow structure in a turbulent boundary layer on the wall pressure signature, the conclusions of these studies have often been limited due to their reliance on a single observation. These single observations include investigations using one signal processing method over a range of locations, or a variety of signal processing techniques at a single location in the boundary layer. Hence, the conclusions often include conjecture on the impact of the flow structure at other physical locations, as well as predictions on the observable effect of flow structure should the turbulence be examined with a different signal processing perspective. In the current study, experimental data of simultaneous wall pressure and velocity fluctuations across the boundary layer have been obtained in a low-noise flow facility. These data have been examined using a variety of signal processing techniques, including probability distributions and spectral analysis. The distinct features of the Reynolds stress within the boundary layer and the observed irrotational motion at the outer edge of the boundary layer were evident in the results from each signal processing method. The influence of these two flow patterns on the wall pressure spectrum was identified and support conjectures made on the correlation between turbulence source locations and frequency bands in the wall pressure spectrum. The investigation demonstrates the necessity and utility of multiple perspectives over a range of spatial locations to study turbulence.
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Gleason, Scott. "A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations." Remote Sensing 11, no. 16 (August 9, 2019): 1858. http://dx.doi.org/10.3390/rs11161858.
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This manuscript describes real-time on-orbit instrument compatible open loop signal tracking techniques for Global Navigation Satellite Systems (GNSS) reflection observations. All GNSS-reflection (GNSS-R) satellite instruments require algorithms which run in real-time on-board the satellite, that are capable of predicting the code phase time delay and Doppler frequency of surface reflected signals. The algorithms presented here are for open loop tracking techniques in reflected GNSS signals for the purposed of making surface remote sensing observations. Initially, the algorithms are demonstrated using high resolution sampled data from the NASA Cyclone GNSS (CYGNSS) mission over ocean and land surfaces. Subsequently. the algorithm performance over ocean regions is analyzed in detail using a larger data set. As part of the analysis, the algorithm is assessed for its speed of convergence, to demonstrate general compatibility with spacecraft instrument processing limitations. Results indicate that over ocean regions is it possible to robustly predict in real time the Doppler frequency and code phase time delay of multiple reflected signal to sufficient precision to make science observations of the scattering surface. These algorithms are intended to provide a baseline technique and variations from which the scientific community can design more specialized algorithms for individual applications.
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Nakamori, S., A. Hermoso-Carazo, and J. Linares-Pérez. "A general smoothing equation for signal estimation using randomly delayed observations in the correlated signal-noise case." Digital Signal Processing 16, no. 4 (July 2006): 369–88. http://dx.doi.org/10.1016/j.dsp.2005.04.013.
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Xing, Jin, Baoguo Yu, Dongkai Yang, Jie Li, Zhejia Shi, Guodong Zhang, and Feng Wang. "A Real-Time GNSS-R System for Monitoring Sea Surface Wind Speed and Significant Wave Height." Sensors 22, no. 10 (May 17, 2022): 3795. http://dx.doi.org/10.3390/s22103795.
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This paper presents a monitoring system based on Global Navigation Satellite System (GNSS) reflected signals to provide real-time observations of sea conditions. Instead of a computer, the system uses a custom-built hardware platform that incorporates Radio Frequency (RF), Field Programmable Gate Array (FPGA), Digital Signal Processing (DSP), and Raspberry Pi for real-time signal processing. The suggested structure completes the navigation signal’s positioning as well as the reflected signal’s feature extraction. Field tests are conducted to confirm the effectiveness of the system and the retrieval algorithm described in this research. The entire system collects and analyzes signals at a coastal site in the field experiment, producing sea surface wind speed and significant wave height (SWH) that are compared to local weather station data, demonstrating the system’s practicality. The system can allow the centralized monitoring of many sites, as well as field experiments and real-time early warning at sea.
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Iarlori, M., F. Madonna, V. Rizi, T. Trickl, and A. Amodeo. "Effective resolution concepts for lidar observations." Atmospheric Measurement Techniques 8, no. 12 (December 10, 2015): 5157–76. http://dx.doi.org/10.5194/amt-8-5157-2015.
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Abstract. Since its establishment in 2000, EARLINET (European Aerosol Research Lidar NETwork) has provided, through its database, quantitative aerosol properties, such as aerosol backscatter and aerosol extinction coefficients, the latter only for stations able to retrieve it independently (from Raman or high-spectral-resolution lidars). These coefficients are stored in terms of vertical profiles, and the EARLINET database also includes the details of the range resolution of the vertical profiles. In fact, the algorithms used in the lidar data analysis often alter the spectral content of the data, mainly acting as low-pass filters to reduce the high-frequency noise. Data filtering is described by the digital signal processing (DSP) theory as a convolution sum: each filtered signal output at a given range is the result of a linear combination of several signal input data samples (relative to different ranges from the lidar receiver), and this could be seen as a loss of range resolution of the output signal. Low-pass filtering always introduces distortions in the lidar profile shape. Thus, both the removal of high frequency, i.e., the removal of details up to a certain spatial extension, and the spatial distortion produce a reduction of the range resolution. This paper discusses the determination of the effective resolution (ERes) of the vertical profiles of aerosol properties retrieved from lidar data. Large attention has been dedicated to providing an assessment of the impact of low-pass filtering on the effective range resolution in the retrieval procedure.
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Nakhmanson, G. S., and D. S. Akinshin. "Detection of the Trajectories of Moving Rectilinearly Air Targets in the Secondary Processing of Radar Information." Journal of the Russian Universities. Radioelectronics 22, no. 5 (December 4, 2019): 61–70. http://dx.doi.org/10.32603/1993-8985-2019-22-5-61-70.
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Introduction. The primary functions of secondary processing of radar information are to detect and maintain the trajectories of air targets (AT). The AT trajectory detection can be characterised by the probability of detecting trajectory and average autocapture time. When the target moves, its distance from the radar station changes, leading to a change in the signal/noise ratio and the probability of detecting AT.Aim. To assess the impact of a change in the probability of detection of a straight and evenly moving target at consecutive time intervals of radar observation upon the characteristics of trajectory detection during secondary processing of radar information.Methods and materials. The research aim was achieved using the methods of mathematical statistics, including verification of statistical hypotheses, assessment of distribution parameters and theory of perturbations by small parameters. The ratio of the distance travelled by the AT during the review period to the target range at the initial moment of its detection was chosen as a perturbation parameter.Results. Analytical expressions were established for the probability of detecting a straight-moving AT and the probability of detecting the trajectory of its movement at interval multiples during the study period. The study illustrated the probability of detecting AT moving away from radar by means of consistent radar observations with reduced signal/noise ratios and angles between the velocity vector and the AT vector radius relative to the radar. The increase in AT speed which causes the z parameter to change from 0.01 to 0.07 reduces the probability of AT detection from 0.727 to 0.52 and leads to a corresponding change in the probability of detecting the trajectory. If the observation time is reduced by one time interval, the probability of detecting the trajectory is from 0.03 to 0.04…0.07 for signal/noise 40 ratio and from 0.06 to 0.08…0.11 for signal/noise 25 ratio (with the probability of false alarm 10–4 ).Conclusion. The resulting expressions allow for the calculation of directly moving AT trajectory detection, considering changes in the probability of detecting targets in successive time intervals of radar observations.
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Castro Pérez, Sarahi Nicole, and Stelian Alexandru Borz. "Improving the Event-Based Classification Accuracy in Pit-Drilling Operations: An Application by Neural Networks and Median Filtering of the Acceleration Input Signal Data." Sensors 21, no. 18 (September 19, 2021): 6288. http://dx.doi.org/10.3390/s21186288.
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Forestry is a complex economic sector which is relying on resource and process monitoring data. Most of the forest operations such as planting and harvesting are supported by the use of tools and machines, and their monitoring has been traditionally done by the use of pen-and-paper time studies. Nevertheless, modern data collection and analysis methods involving different kinds of platforms and machine learning techniques have been studied lately with the aim of easing the data management process. By their outcomes, improvements are still needed to reach a close to 100% activity recognition, which may depend on several factors such as the type of monitored process and the characteristics of the signals used as inputs. In this paper, we test, thought a case study on mechanized pit-drilling operations, the potential of digital signal processing techniques combined with Artificial Neural Networks (ANNs) in improving the event-based classification accuracy in the time domain. Signal processing was implemented by the means of median filtering of triaxial accelerometer data (window sizes of 3, 5, and up to 21 observations collected at 1 Hz) while the ANNs were subjected to the regularization hyperparameter’s tunning. An acceleration signal processed by a median filter with a window size of 3 observations and fed into an ANN set to learn and generalize by a regularization parameter of α = 0.01 has been found to be the best strategy in improving the event-based classification accuracy (improvements of 1% to 8% in classification accuracy depending on the type of event in question). Improvement of classification accuracy by signal filtering and ANN tuning may depend largely on the type of monitored process and its outcomes in terms of event duration; therefore, other monitoring applications may need particular designs of signal processing and ANN tuning.
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Hernandez-Gonzalez, M., M. V. Basin, and A. G. Loukianov. "Optimal Filtering for Polynomial Systems over Switching Delayed Observations." Circuits, Systems, and Signal Processing 32, no. 5 (March 13, 2013): 2353–70. http://dx.doi.org/10.1007/s00034-013-9571-x.
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Stanislavsky, L. "METHODS OF RADIO FREQUENCY INTERFERENCE MITIGATION ON THE STAGE OF PRELIMINARY PROCESSING OF RECEIVED SIGNALS." RADIO PHYSICS AND RADIO ASTRONOMY 27, no. 4 (2022): 268–83. http://dx.doi.org/10.15407/rpra27.04.268.
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Subject and Purpose.Methods for computer processing of radio astronomical signals observed with space objects at low frequencies are given. The aim of this paper is to improve the current methods and use their combinations for cleaning records from radio interference of natural and artificial origin in the frequency-time domain, as well as to discuss advantages and disadvantage of the methods. Methods and Methodology.In the study of records obtained with radio astronomical observations there is a common feature of received signals from space sources, which consists in a significant contribution of radio interference. Having sufficient experience on possible types of interference and distortion of signals on the way of their propagation, the efficiency of suggested procedures, clearing radio signal interference in the frequency-time domain by a combination of different approaches in dependence from typical features of signals withinvestigated space objects, is shown. Results. The developed methods of extracting space signals against the background of interference allow one to get unique data on the sources of radio emission in astrophysical phenomena. On the one hand, software tools make it possible to detect very weak events against the background of radio frequency interference. On the other hand, they allow one to measureemission parameters based on the most statistically complete set of events. Conclusions.The results obtained in this work manifest that there is no universal way to overcome any obstacle in the records of radio astronomical observations because of radio interference. In addition, even if the most appropriate method is applied, it often requires pre-adjustment of the corresponding parameters on which the analysis of physical parameters of radio emission in the area of generation depends. But if such a space signal at the radio records is not very spoiled by interference, the use of considered methods can be successful and useful.
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Wietfeldt, R., W. Van Straten, D. Del Rizzo, N. Bartel, W. Cannon, M. Bailes, J. Reynolds, and W. Wilson. "The S2 Baseband Processing System for Phase-coherent Pulsar Observations." International Astronomical Union Colloquium 160 (1996): 21–22. http://dx.doi.org/10.1017/s0252921100040926.
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AbstractThe phase-coherent recording of pulsar data and subsequent software dispersion removal provide a flexible way to reach the limits of high time resolution, useful for more precise pulse timing and the study of fast signal fluctuations within a pulse. Because of the huge data rate and lack of adequate recording and computing capabilities, this technique has been used mostly only for small pulsar data sets. In recent years, however, the development of very capable, reasonably inexpensive high-speed recording systems and computers has made feasible the notion of pulsar baseband recording and subsequent processing with a workstation/computer. In this paper we discuss the development of a phase-coherent baseband processing system for radio pulsar observations. This system is based on the S2 VLBI recorder developed at ISTS/York University in Toronto, Canada. We present preliminary first results for data from the Vela pulsar, obtained at Parkes, Australia, and processed at ISTS/York University, and discuss plans for future developments.
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Chang, Haowei, Chunlei Pang, Liang Zhang, and Zehui Guo. "Rotating Single-Antenna Spoofing Signal Detection Method Based on IPNN." Sensors 22, no. 19 (September 21, 2022): 7141. http://dx.doi.org/10.3390/s22197141.
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The traditional carrier-phase differential detection technology mainly relies on the spatial processing method, which uses antenna arrays or moving antennas to detect spoofing signals, but it cannot be applied to static single-antenna receivers. Aiming at this problem, this paper proposes a rotating single-antenna spoofing signal detection method based on the improved probabilistic neural network (IPNN). When the receiver antenna rotates at a constant speed, the carrier-phase double difference of the real signal will change with the incident angle of the satellite. According to this feature, the classification and detection of spoofing signals can be realized. Firstly, the rotating single-antenna receiver collects carrier-phase values and performs double-difference processing. Then, we construct an IPNN model, whose smoothing factor can be adaptively adjusted according to the type of failure mode. Finally, we use the IPNN model to realize the classification and processing of the carrier-phase double-difference observations and obtain the deception detection results. In addition, in order to reflect that the method has a certain practical value, we simulate the spoofing scenario of satellite signals and effectively identify abnormal satellite signals according to the detection results of the inter-satellite differential combination. Actual experiments indicate that the detection accuracy of the proposed method for spoofing signals reaches 98.84%, which is significantly better than the classical probabilistic neural network (PNN) and back-propagation neural network (BPNN), and the method can be implemented in fixed base station receivers for the real-time detection of forwarding spoofing.
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Jahromi, O. S., B. A. Francis, and R. H. Kwong. "Spectrum Estimation Using Multirate Observations." IEEE Transactions on Signal Processing 52, no. 7 (July 2004): 1878–90. http://dx.doi.org/10.1109/tsp.2004.828941.
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Li, Weiqiang, Estel Cardellach, Serni Ribó, Santi Oliveras, and Antonio Rius. "Exploration of Multi-Mission Spaceborne GNSS-R Raw IF Data Sets: Processing, Data Products and Potential Applications." Remote Sensing 14, no. 6 (March 10, 2022): 1344. http://dx.doi.org/10.3390/rs14061344.
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Earth reflected Global Navigation Satellite System (GNSS) signals can be received by dedicated orbital receivers for remote sensing and Earth observation (EO) purposes. Different spaceborne missions have been launched during the past years, most of which can only provide the delay-Doppler map (DDM) of the power of the reflected GNSS signals as their main data products. In addition to the power DDM products, some of these missions have collected a large amount of raw intermediate frequency (IF) data, which are the bit streams of raw signal samples recorded after the analog-to-digital converters (ADCs) and prior to any onboard digital processing. The unprocessed nature of these raw IF data provides an unique opportunity to explore the potential of GNSS Reflectometry (GNSS-R) technique for advanced geophysical applications and future spaceborne missions. To facilitate such explorations, the raw IF data sets from different missions have been processed by Institute of Space Sciences (ICE-CSIC, IEEC), and the corresponding data products, i.e., the complex waveform of the reflected signal, have been generated and released through our public open-data server. These complex waveform data products provide the measurements from different GNSS constellations (e.g., GPS, Galileo and BeiDou), and include both the amplitude and carrier phase information of the reflected GNSS signal at higher sampling rate (e.g., 1000 Hz). To demonstrate these advanced features of the data products, different applications, e.g., inland water detection and surface altimetry, are introduced in this paper. By making these complex waveform data products publicly available, new EO capability of the GNSS-R technique can be further explored by the community. Such early explorations are also relevant to ESA’s next GNSS-R mission, HydroGNSS, which will provide similar complex observations operationally and continuously in the future.
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Dawidowicz, Karol. "Analyzing the Impact of Different Pcv Calibration Models on Height Determination Using Gps/Glonass Observations from Asg-Eupos Network." Artificial Satellites 49, no. 4 (December 1, 2014): 211–23. http://dx.doi.org/10.2478/arsa-2014-0016.
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ABSTRACT The integration of GPS with GLONASS is very important in satellite-based positioning because it can clearly improve reliability and availability. However, unlike GPS, GLONASS satellites transmit signals at different frequencies. This results in significant difficulties in modeling and ambiguity resolution for integrated GNSS positioning. There are also some difficulties related to the antenna Phase Center Variations (PCV) problem because, as is well known, the PCV is dependent on the received signal frequency dependent. Thus, processing simultaneous observations from different positioning systems, e.g. GPS and GLONASS, we can expect complications resulting from the different structure of signals and differences in satellite constellations. The ASG-EUPOS multifunctional system for precise satellite positioning is a part of the EUPOS project involving countries of Central and Eastern Europe. The number of its users is increasing rapidly. Currently 31 of 101 reference stations are equipped with GPS/GLONASS receivers and the number is still increasing. The aim of this paper is to study the height solution differences caused by using different PCV calibration models in integrated GPS/GLONASS observation processing. Studies were conducted based on the datasets from the ASG-EUPOS network. Since the study was intended to evaluate the impact on height determination from the users’ point of view, a so-called “commercial” software was chosen for post-processing. The analysis was done in a baseline mode: 3 days of GNSS data collected with three different receivers and antennas were used. For the purposes of research the daily observations were divided into different sessions with a session length of one hour. The results show that switching between relative and absolute PCV models may cause an obvious effect on height determination. This issue is particularly important when mixed GPS/GLONASS observations are post-processed.
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van Oosterom, A., and G. J. H. Uijen. "The Performance of Information-Theoretic Criteria in Detecting the Number of Independent Signals in Multilead ECGs." Methods of Information in Medicine 31, no. 04 (1992): 256–62. http://dx.doi.org/10.1055/s-0038-1634887.
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Abstract:Three different methods to detect the number of independent signals in multilead ECGs were evaluated by using different ECG realizations with known specifications for signal and noise: a threshold method (TM), the minimum description length (MDL) and Akaike’s information criterion (AIC).The fundamental assumption in this kind of signal processing is that both the signal and the noise stem from independent stochastic generators. The consequence is that the detection of the number of signals is only possible if the noise is white, or if the noise properties have been specified.The evaluation was performed with respect to the QRS complex of individual multilead ECG simulations and to the entire ensemble. In the simulated ECGs the number of independent signals was fixed (eight). It was found that, out of the three methods studied, the performance of MDL was the best, especially when the number of available observations in the noise (used to estimate the noise specifications) was moderate.
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Zhao, Hongyang, Jing Jin, Longqi Wang, Bingjie Shan, Yi Shen, and Yu Jiang. "A Pulsar Search Method Combining a New Feature Representation and Convolutional Neural Network*." Astrophysical Journal 929, no. 1 (April 1, 2022): 18. http://dx.doi.org/10.3847/1538-4357/ac52ef.
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Abstract The radiation energy of X-ray pulsars is mainly concentrated in the high-energy ray bands, so processing high-energy photon signals is helpful for discovering some young and active pulsars. To quickly and accurately detect effective pulsar signals from a large number of samples within a finite observation time, an automatic identification algorithm for pulsar candidates based on X-ray observations is developed in this paper. First, the autocorrelation operation is used to improve the signal-to-noise ratio of the profile and solve the initial phase misalignment problem. Then, the candidate frequency range is expanded, and the output signal is folded according to these frequencies to obtain a series of profiles. The six statistical features of these profiles are extracted to generate frequency-feature curves. Compared with the traditional epoch folding method, the frequency-feature curves show more consistent characteristics. To improve the classification accuracy, the frequency-feature curves are converted into two-dimensional images, and ConvNets are used for deep feature extraction and classification. A simulation method based on the nonhomogeneous Poisson process is utilized to create the training set, and generative adversarial networks are used for data augmentation to solve the class imbalance problem caused by limited pulsar samples. Finally, the RXTE observation data of PSR B0531+21, PSR B0540-69, and PSR B1509-58 are selected for testing. The experimental results show that the highest recall and precision reached 0.996 and 0.983, respectively. Demonstrating the considerable potential of this method for identifying pulsar candidates based on X-ray observations.
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WANG, YANFEI, CHANGCHUN YANG, and JINGJIE CAO. "ON TIKHONOV REGULARIZATION AND COMPRESSIVE SENSING FOR SEISMIC SIGNAL PROCESSING." Mathematical Models and Methods in Applied Sciences 22, no. 02 (February 2012): 1150008. http://dx.doi.org/10.1142/s0218202511500084.
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Using compressive sensing and sparse regularization, one can nearly completely reconstruct the input (sparse) signal using limited numbers of observations. At the same time, the reconstruction methods by compressing sensing and optimizing techniques overcome the obstacle of the number of sampling requirement of the Shannon/Nyquist sampling theorem. It is well known that seismic reflection signal may be sparse, sometimes and the number of sampling is insufficient for seismic surveys. So, the seismic signal reconstruction problem is ill-posed. Considering the ill-posed nature and the sparsity of seismic inverse problems, we study reconstruction of the wavefield and the reflection seismic signal by Tikhonov regularization and the compressive sensing. The l0, l1 and l2 regularization models are studied. Relationship between Tikhonov regularization and the compressive sensing is established. In particular, we introduce a general lp - lq (p, q ≥ 0) regularization model, which overcome the limitation on the assumption of convexity of the objective function. Interior point methods and projected gradient methods are studied. To show the potential for application of the regularized compressive sensing method, we perform both synthetic seismic signal and field data compression and restoration simulations using a proposed piecewise random sub-sampling. Numerical performance indicates that regularized compressive sensing is applicable for practical seismic imaging.
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Tkachuk, O. V. "OPTIMAL IMAGE SIGNALS PROCESSING ON THE NOISE BACKGROUND IN THE INFORMATION SYSTEM WITH ADAPTIVE ANTENNA ARRAY." Key title Zbìrnik naukovih pracʹ Odesʹkoï deržavnoï akademìï tehnìčnogo regulûvannâ ta âkostì -, no. 2(17) (2020): 29–36. http://dx.doi.org/10.32684/2412-5288-2020-2-17-29-36.
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The method to restore image signals against the arbitrary intensity noise background in the information radio engineering system with adaptive antenna array has been developed. In order to use methods developed for processing one-dimensional signals for image recovery in the information system with adaptive antenna array, the transition from the two-dimensional array to vector representation is carried out. Mathematical model of narrowband signal formed at input of antenna array elements in space-time sense is obtained. Correlation matrices of image carrier signals, interference and noise are considered and features of adaptive processing of image signals coming from several sources are observed. An expression was found for the likelihood function if the incoming vector process is a multivariate stationary Gaussian process with a non-zero mean. According to the maximum likelihood criterion, the expression for the system of optimal independent parametric weight vectors necessary for image signals restoring against the arbitrary intensity noise background coming from several independent sources in the information system with adaptive antenna array is obtained. In accordance to this system, a signal processing algorithm is built in the adaptive processor of N-dimensional adaptive antenna array. A simulation model of image signal restores coming from one source in the information system with adaptive antenna array against the arbitrary intensity noise background coming from several independent sources is built. It is shown that the use of weight coefficients calculated on the basis of the correlation matrix of observations, due to its properties, does not allow dividing the set of correlated image signals. The direction of further development of the obtained results in the class of invariant methods is determined.
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Beser, Katarzyna, Maaijke Mevius, Marcin Grzesiak, and Hanna Rothkaehl. "Detection of Periodic Disturbances in LOFAR Calibration Solutions." Remote Sensing 14, no. 7 (April 2, 2022): 1719. http://dx.doi.org/10.3390/rs14071719.
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The Earth’s ionosphere is a highly variable medium on a wide range of spatio-temporal scales. The responsiveness of plasma to the geomagnetic field and its changes gives rise to anisotropy, which may introduce wave-like characteristics while scanning the ionosphere with a line-of-sight towards a radio source. Previous studies of LOw Frequency ARray (LOFAR) calibration phase solutions report that the estimated beta parameter of a structure function calculated over 6–8 h of astronomical observation timespan has a range of values from 1.6 to 2.0, with an average of 1.89. Such difference between the observations could result from transient wave-like disturbances within the data. This study aims to present a method of signal processing of ionospheric calibration datasets that allows the extraction of a transient wave-like signal and discuss its possible origin. We use complex Morlet wavelet analysis applied to two 8 h observations corresponding to very quiet geomagnetic conditions. We find a wave-like signal in the interferometric Total Electron Content data even during periods of no geomagnetic activity. We suggest it results from the relative velocity changes between the LOFAR line-of-sight and a convection pattern in the ionospheric F layer. Establishing the relationship between quiet time ionosphere, geomagnetic field changes and LOFAR’s calibration solutions may prove beneficial to determination of the dominant signals in the more disturbed conditions, which we leave for future study.
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Kuze, A., H. Suto, K. Shiomi, T. Urabe, M. Nakajima, J. Yoshida, T. Kawashima, Y. Yamamoto, F. Kataoka, and H. Buijs. "Level 1 algorithms for TANSO on GOSAT: processing and on-orbit calibrations." Atmospheric Measurement Techniques 5, no. 10 (October 19, 2012): 2447–67. http://dx.doi.org/10.5194/amt-5-2447-2012.
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Abstract. The Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT) (nicknamed "Ibuki") has been providing global space-borne observations of carbon dioxide (CO2) and methane (CH4) since 2009. In this paper, we first describe the version V150.151 operational Level 1 algorithms that produce radiance spectra from the acquired interferograms. Second, we will describe the on-orbit characteristics and calibration of TANSO-FTS. Overall function and performance such as signal to noise ratio and spectral resolution are within design objectives. Correction methods of small on-orbit degradations and anomalies, which have been found since launch, are described. Lastly, calibration of TANSO Cloud and Aerosol Imager (TANSO-CAI) are summarized.
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Ohno, M., K. Goto, Y. Hanabata, H. Takahashi, Y. Fukazawa, M. Yoshino, T. Saito, et al. "Development of signal processing system of avalanche photo diode for space observations by Astro-H." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 699 (January 2013): 112–15. http://dx.doi.org/10.1016/j.nima.2012.03.022.
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Mathews, J. D., J. Doherty, C. H. Wen, S. J. Briczinski, D. Janches, and D. D. Meisel. "An update on UHF radar meteor observations and associated signal processing techniques at Arecibo Observatory." Journal of Atmospheric and Solar-Terrestrial Physics 65, no. 10 (July 2003): 1139–49. http://dx.doi.org/10.1016/j.jastp.2003.07.009.
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Jameson, A. R. "Radar Observations of Rainfall Variability Using Non-Rayleigh Signal Fluctuations." Journal of Applied Meteorology and Climatology 47, no. 2 (February 1, 2008): 607–19. http://dx.doi.org/10.1175/2007jamc1630.1.
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Abstract The spatial variability and temporal variability of precipitation are widely recognized. In particular, rainfall rates can fluctuate widely in regions where the raindrops are clustered and where mean conditions are changing (statistical heterogeneity). Indeed, at times, the ambiguity associated with an estimated average rainfall rate may become very large. Therefore, in quantitative measurements of precipitation, it would be useful to identify where this occurs. In this work a technique is proposed and applied to quantify the variability in rainfall rates introduced by statistical heterogeneity and raindrop clustering using deviations from Rayleigh statistics of intensity fluctuations. This technique separates the Rayleigh contributions to the observed relative dispersion from those arising from clustering and statistical heterogeneities. Applications to conventional meteorological radar measurements are illustrated using two scans. Often, but not always, the greatest ambiguities in estimates of the average rainfall rate occur just where the rainfall rates are the largest and presumably where accurate estimates are most important. This ambiguity is not statistical; rather, it indicates the presence of important sub-beam-scale fluctuations. As a consequence, no single average value can be applied uniformly to the entire domain. The examples provided here also demonstrate that the appropriate observations are feasible using current conventional meteorological radars with adequate processing capabilities. However, changes in radar technology that improve and increase pulse-to-pulse statistical independence will permit such observations to be gathered more routinely at finer spatial resolution and with enhanced precision.
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Pellinen-Wannberg, A. "Meteor head echoes - observations and models." Annales Geophysicae 23, no. 1 (January 31, 2005): 201–5. http://dx.doi.org/10.5194/angeo-23-201-2005.
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Abstract. Meteor head echoes - instantaneous echoes moving with the velocities of the meteors - have been recorded since 1947. Despite many attempts, this phenomenon did not receive a comprehensive theory for over 4 decades. The High Power and Large Aperture (HPLA) features, combined with present signal processing and data storage capabilities of incoherent scatter radars, may give an explanation for the old riddle. The meteoroid passage through the radar beam can be followed with simultaneous spatial-time resolution of about 100m-ms class. The current views of the meteor head echo process will be presented and discussed. These will be related to various EISCAT observations, such as dual-frequency target sizes, altitude distributions and vector velocities.
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Kornprobst, Jonas, Alexander Paulus, Josef Knapp, and Thomas F. Eibert. "Phase Retrieval for Partially Coherent Observations." IEEE Transactions on Signal Processing 69 (2021): 1394–406. http://dx.doi.org/10.1109/tsp.2021.3057261.
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Peng, Fangrong, and Biao Chen. "Decentralized Estimation With Dependent Gaussian Observations." IEEE Transactions on Signal Processing 65, no. 5 (March 1, 2017): 1172–82. http://dx.doi.org/10.1109/tsp.2016.2631463.
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Setlak, Lucjan, and Rafał Kowalik. "Study and Analysis of Interference Signals of the LTE System of the GNSS Receiver." Sensors 21, no. 14 (July 19, 2021): 4901. http://dx.doi.org/10.3390/s21144901.
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Sometimes, it is impossible to conduct tests with the use of the GNSS system, or the obtained results of the measurements made differ significantly from the predicted accuracy. The most common cause of the problems (external factors, faulty results) are interference disturbances from other radio telecommunication systems. The subject of this paper is to conduct research, the essence of which is an in-depth analysis in the field of elimination of LTE interference signals of the GNSS receiver, that is based on the developed effective methods on counteracting the phenomenon of interference signals coming from this system and transmitted on the same frequency. Interference signals are signals transmitted in the GNSS operating band, and unwanted signals may cause incorrect processing of the information provided to the end-user about his position, speed, and current time. This article presents methods of identifying and detecting interference signals, with particular emphasis on methods based on spatial processing of signals transmitted by the LTE system. A comparative analysis of the methods of detecting an unwanted signal was made in terms of their effectiveness and complexity of their implementation. Moreover, the concept of a new comprehensive anti-interference solution was proposed. It includes, among others, information on the various stages of GNSS signal processing in the proposed system, in relation to the algorithms used in traditional GNSS receivers. The final part of the article presents the obtained research results and the resulting significant observations and practical conclusions.
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Díaz Baso, C. J., J. de la Cruz Rodríguez, and S. Danilovic. "Solar image denoising with convolutional neural networks." Astronomy & Astrophysics 629 (September 2019): A99. http://dx.doi.org/10.1051/0004-6361/201936069.
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The topology and dynamics of the solar chromosphere are greatly affected by the presence of magnetic fields. The magnetic field can be inferred by analyzing polarimetric observations of spectral lines. Polarimetric signals induced by chromospheric magnetic fields are, however, particularly weak, and in most cases very close to the detection limit of current instrumentation. Because of this, there are only few observational studies that have successfully reconstructed the three components of the magnetic field vector in the chromosphere. Traditionally, the signal-to-noise ratio of observations has been improved by performing time-averages or spatial averages, but in both cases, some information is lost. More advanced techniques, like principal-component analysis, have also been employed to take advantage of the sparsity of the observations in the spectral direction. In the present study, we use the spatial coherence of the observations to reduce the noise using deep-learning techniques. We designed a neural network that is capable of recovering weak signals under a complex noise corruption (including instrumental artifacts and non-linear post-processing). The training of the network is carried out without a priori knowledge of the clean signals, or an explicit statistical characterization of the noise or other corruption. We only use the same observations as our generative model. The performance of this method is demonstrated on both synthetic experiments and real data. We show examples of the improvement in typical signals obtained in current telescopes such as the Swedish 1 m Solar Telescope. The presented method can recover weak signals equally well no matter what spectral line or spectral sampling is used. It is especially suitable for cases when the wavelength sampling is scarce.
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Duc Son Pham and A. M. Zoubir. "Estimation of Multicomponent Polynomial Phase Signals With Missing Observations." IEEE Transactions on Signal Processing 56, no. 4 (April 2008): 1710–15. http://dx.doi.org/10.1109/tsp.2007.909345.
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Fedorov, Roman, and Oleg Berngardt. "Monitoring observations of meteor echo at the EKB ISTP SB RAS radar: algorithms, validation, statistics." Solar-Terrestrial Physics 7, no. 1 (March 29, 2021): 47–58. http://dx.doi.org/10.12737/stp-71202107.
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The paper considers the implementation of algorithms for automatic search for signals scattered by meteor trails according to EKB ISTP SB RAS radar data. In general, the algorithm is similar to the algorithms adopted in specialized meteor systems. The algorithm is divided into two stages: detecting a meteor echo and determining its parameters. We show that on the day of the maximum Geminid shower, December 13, 2016, the scattered signals detected by the algorithm are foreshortening and correspond to scattering by irregularities extended in the direction of the meteor shower radiant. This confirms that the source of the signals detected by the algorithm is meteor trails. We implement an additional program for indirect trail height determination. It uses a decay time of echo and the NRLMSIS-00 atmosphere model to estimate the trail height. The dataset from 2017 to 2019 is used for further testing of the algorithm. We demonstrate a correlation in calculated Doppler velocity between the new algorithm and FitACF. We present a solution of the inverse problem of reconstructing the neutral wind velocity vector from the data obtained by the weighted least squares method. We compare calculated speeds and directions of horizontal neutral winds, obtained in the three-dimensional wind model, and the HWM-14 horizontal wind model. The algorithm allows real-time scattered signal processing and has been put into continuous operation at the EKB ISTP SB RAS radar.
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McFadden, P. L., B. J. Drummond, and S. Kravis. "The Nth‐root stack: Theory, applications, and examples." GEOPHYSICS 51, no. 10 (October 1986): 1879–92. http://dx.doi.org/10.1190/1.1442045.
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Multichannel geophysical data are usually stacked by calculating the average of the observations on all channels. In the Nth‐root stack, the average of the Nth root of each observation is raised to the Nth power, with the signs of the observations and average maintained. When N = 1, the process is identical to conventional linear stacking or averaging. Nth‐root stacking has been applied in the processing of seismic refraction and teleseismic array data. In some experiments and certain applications it is inferior to linear stacking, but in others it is superior. Although the variance for an Nth‐root stack is typically less than for a linear stack, the mean square error is larger, because of signal attenuation. The fractional amount by which the signal is attenuated depends in a complicated way on the number of data channels, the order (N) of the stack, the signal‐to‐noise ratio, and the noise distribution. Because the signal‐to‐noise ratio varies across a wavelet, peaking where the signal is greatest and approaching zero at the zero‐crossing points, the attenuation of the signal varies across a wavelet, thereby producing signal distortion. The main visual effect of the distortion is a sharpening of the legs of the wavelet. However, the attenuation of the signal is accompanied by a much greater attenuation of the background noise, leading to a significant contrast enhancement. It is this sharpening of the signal, accompanied by the contrast enhancement, that makes the technique powerful in beam‐steering applications of array data. For large values of N, the attenuation of the signal with low signal‐to‐noise ratios ultimately leads to its destruction. Nth‐root stacking is therefore particularly powerful in applications where signal sharpening and contrast enhancement are important but signal distortion is not.