Relevant bibliographies by topics / Doppler Signals

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Doppler Signals'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Doppler Signals"

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Fengzhen, Zhang, Li Guijuan, Zhang Zhaohui, and Hu Chen. "Doppler shift extraction of wideband signal using spectrum scaling matching." MATEC Web of Conferences 208 (2018): 01001. http://dx.doi.org/10.1051/matecconf/201820801001.

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Doppler shift is an important feature of moving targets. It can be used to extract target velocity, distance, track and other movement parameters. According to the problem of extracting Doppler shift for wideband signals with unstable line spectrum or no line spectrum, we proposed a Doppler shift extraction method for wideband signals based on spectral scaling matching. Firstly, a spectrum reference matrix corresponding to different relative Doppler shift is generated. Then, the matching degree of Doppler signal spectrum and reference matrix is measured by linear correlation coefficient. Finally, the Doppler shift of wideband signals is extracted through matching degree optimization. Simulation results show that the proposed method can extract the Doppler shift characteristics of wideband signals effectively.
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Li, Wenchao, Gangyao Kuang, and Boli Xiong. "Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD." Applied Sciences 8, no. 10 (October 2, 2018): 1801. http://dx.doi.org/10.3390/app8101801.

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Micro-Doppler signals analysis has been emerging as an important topic in target identification, and relative research has been focusing on features extraction and separation of the radar signals. As a time-frequency representation, the Hilbert-Huang transform (HHT) could extract the accurate instantaneous micro-Doppler signature from the radar signals by empirical mode decomposition and Hilbert transform. However, HHT has the shortcoming that it cannot decompose the signals with close-frequency components. To solve this problem, an innovative decomposition method for multicomponent micro-Doppler signals based on Hilbert–Huang transform and analytical mode decomposition (HHT-AMD) is proposed. In this method, the multicomponent micro-Doppler signals are firstly decomposed by empirical mode decomposition, and the decomposed signal components are transformed by Hilbert transform to get the Hilbert-Huang spectrum and marginal spectrum. Through the spectrum processing, we get the frequency distribution of each signal component. The next step is to judge whether there exists frequency aliasing in each signal component. If there is aliasing, the AMD method is used to decompose the signal until all the decomposed signals are mono-component signals. Evaluation considerations are covered with numerical simulations and experiments on measured radar data. The results demonstrate that compared with conventional HHT, the proposed method yields accurate decomposition for multicomponent micro-Doppler signals and improves the robustness of decomposition. The method presented here can also be applied in various settings of non-stationary signal analysis and filtering.
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Gong, Jiangkun, Jun Yan, Deren Li, and Deyong Kong. "Detection of Micro-Doppler Signals of Drones Using Radar Systems with Different Radar Dwell Times." Drones 6, no. 9 (September 19, 2022): 262. http://dx.doi.org/10.3390/drones6090262.

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Not any radar dwell time of a drone radar is suitable for detecting micro-Doppler (or jet engine modulation, JEM) produced by the rotating blades in radar signals of drones. Theoretically, any X-band drone radar system should detect micro-Doppler of blades because of the micro-Doppler effect and partial resonance effect. Yet, we analyzed radar data detected by three radar systems with different radar dwell times but similar frequency and velocity resolution, including Radar−α, Radar−β, and Radar−γ with radar dwell times of 2.7 ms, 20 ms, and 89 ms, respectively. The results indicate that Radar−β is the best radar for detecting micro-Doppler (i.e., JEM signals) produced by the rotating blades of a quadrotor drone, DJI Phantom 4, because the detection probability of JEM signals is almost 100%, with approximately 2 peaks, whose magnitudes are similar to that of the body Doppler. In contrast, Radar−α can barely detect any micro-Doppler, and Radar−γ detects weak micro-Doppler signals, whose magnitude is only 10% of the body Doppler’s. Proper radar dwell time is the key to micro-Doppler detection. This research provides an idea for designing a cognitive micro-Doppler radar by changing radar dwell time for detecting and tracking micro-Doppler signals of drones.
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Zhang, Shangbin, Qingbo He, Haibin Zhang, Kesai Ouyang, and Fanrang Kong. "Signal separation and correction with multiple Doppler acoustic sources for wayside fault diagnosis of train bearings." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 14 (March 22, 2016): 2664–80. http://dx.doi.org/10.1177/0954406216639342.

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The extraction of single train signal is necessary in wayside fault diagnosis because the acoustic signal acquired by a microphone is composed of multiple train bearing signals and noises. However, the Doppler distortion in the signal acquired by a microphone effectively hinders the signal separation and fault diagnosis. To address this issue, we propose a novel method based on the generalized S-transform, morphological filtering, and time–frequency amplitude matching-based resampling time series for multiple-Doppler-acoustic-source signal separation and correction. First, the original time–frequency distribution is constructed by applying generalized S-transform to the raw signal acquired by a microphone. Based on a morphological filter, several time–frequency distributions corresponding to different single source Doppler fault signals are extracted from the original time–frequency distribution. Subsequently, the time–frequency distributions are reverted to time signals by inverse generalized S-transform. Then, a resampling time series is built by time–frequency amplitude matching to obtain the correct signals without Doppler distortion. Finally, the bearing fault is diagnosed by the envelope spectrum of the correction signal. The effectiveness of this method is verified by simulated and practical signals.
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Grenier, N., F. Basseau, M. Rey, and L. LaGoarde-Segot. "Interpretation of Doppler signals." European Radiology 11, no. 8 (August 2001): 1295–307. http://dx.doi.org/10.1007/s003300100913.

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Zhang, Da, and Ranglei Liu. "Laser Doppler Signal Denoising Based on Wavelet Packet Thresholding Method." International Journal of Optics 2019 (November 14, 2019): 1–11. http://dx.doi.org/10.1155/2019/1097292.

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In laser Doppler velocimeter (LDV), calculation precision of Doppler shift is affected by noise contained in Doppler signal. In order to restrain the noise interference and improve the precision of signal processing, wavelet packet threshold denoising methods are proposed. Based on the analysis of Doppler signal, appropriate threshold function and decomposition layer number are selected. Heursure, sqtwolog, rigrsure, and minimaxi rules are adopted to get the thresholds. Processing results indicate that signal-to-noise ratio (SNR) and root mean square error (RMSE) of simulated signals with original SNR of 0 dB, 5 dB, and 10 dB in both low- and high-frequency ranges are significantly improved by wavelet packet threshold denoising. A double-beam and double-scattering LDV system is built in our laboratory. For measured signals obtained from the experimental system, the minimum relative error of denoised signal is only 0.079% (using minimaxi rule). The denoised waveforms of simulated and experimental signals are much more smooth and clear than that of original signals. Generally speaking, denoising effects of minimaxi and saqtwolog rules are better than those of heursure and rigrsure rules. As shown in the processing and analysis of simulated and experimental signals, denoising methods based on wavelet packet threshold have ability to depress the noise in laser Doppler signal and improve the precision of signal processing. Owing to its effectiveness and practicability, wavelet packet threshold denoising is a practical method for LDV signal processing.
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Dong, Shao Feng, Bao Qiang Du, and Wei Zhou. "Real-Time Measurement Method of Doppler Based on GPS Carrier Signals." Applied Mechanics and Materials 226-228 (November 2012): 2050–55. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.2050.

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According to Doppler effect of satellite on the time synchronization technology between satellite and the ground station, a real-time measurement method of Doppler is proposed based on GPS carrier signals. Using Doppler observations from GPS receiver, the method can real-timely measure Doppler frequency shift of GPS including dynamic Doppler and media Doppler whose error can be timely modified in the receiver end. Simulation results show that the frequency shift caused by dynamic Doppler, a main influencing factor in the course of transmission of time-frequency signal by GPS satellite, is between plus or minus several thousands Hz. Comparing to traditional measurement method of Doppler, the method makes it possible to fast track phase of signal in large dynamic range in synchronous technology.
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Jedelsky, Jan, Milan Maly, Ondrej Cejpek, Graham Wigley, and James F. Meyers. "Software-based processing system for phase Doppler systems." EPJ Web of Conferences 264 (2022): 01019. http://dx.doi.org/10.1051/epjconf/202226401019.

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A Monte Carlo simulation of Phase Doppler systems has been developed. It consists of three sections, the droplet flow description, generation of the photomultiplier signals and then their processing to determine droplet velocities and the time shift between the signals from the three scattered light detection apertures. With highly realistic Doppler bursts being simulated and processed, the question arises as to whether the signal processing software could be used to process ‘real-world’ experimental signals. In a preliminary assessment of its capabilities in such a situation, actual spray Doppler signals (from a Dantec fibre-based PDA system with a BSA signal processor) were recorded and used as input to the software signal processor. The signals from the three photomultipliers were input first into a Picoscope and then into the BSA processor. In this way droplet velocities and size estimates would be available from the BSA as control data. The signal outputs were taken as csv files, and input directly into the software signal processor. Initially the software determined the time location of the centre of each signal burst envelop. This approach was shown to measure signal delays from single cycle to multiple cycles. For this experiment, the software was modified by adding a zero-crossing approach to measure the single cycle delays. The introduction of this method should establish the accuracy of the complete software package in the real world as the results from the preliminary experiment show good agreement between the two techniques.
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Yan, Jun, Huiping Hu, Jiangkun Gong, Deyong Kong, and Deren Li. "Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types." Drones 7, no. 4 (April 21, 2023): 280. http://dx.doi.org/10.3390/drones7040280.

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In this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with only puller blades, multi-rotor drones with only lifting blades, and hybrid vertical take-off and landing (VTOL) fixed-wing drones with both lifting and puller blades. We quantify the radar signatures of the three drones using statistical measures, such as signal-to-noise ratio (SNR), signal-to-clutter ratio (SCR), Doppler speed, Doppler frequency difference (DFD), and Doppler magnitude ratio (DMR). Our findings show that the micro-Doppler signals of lifting blades in all three drone types were stronger than those of puller blades. Specifically, the DFD and DMR values of pusher blades were below 100 Hz and 0.3, respectively, which were much smaller than the 200 Hz and 0.8 values for lifting blades. The micro-Doppler signals of the puller blades were weaker and more stable than those of the lifting blades. Our study demonstrates the potential of using micro-Doppler signatures modulated by different blades for improving drone detection and the identification of drone types by drone detection radar.
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Ericson, Mark A., and Lawrence L. Feth. "Detection of Doppler‐like signals." Journal of the Acoustical Society of America 103, no. 5 (May 1998): 3083. http://dx.doi.org/10.1121/1.422913.

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Dissertations / Theses on the topic "Doppler Signals"

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Colosimo, Joseph William. "Doppler channel emulation of high-bandwidth signals." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85698.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 97).
The Airborne Networks Group at MIT Lincoln Laboratory has funded the construction of a channel emulator capable of applying, in real-time, environmental models to communications equipment in order to test the robustness of new wireless communications algorithms in development. Specific design goals for the new emulator included support for higher bandwidth capabilities than commercial channel emulators and the creation of a flexible framework for future implementation of more complex channel models. Following construction of the emulator's framework, a module capable of applying Doppler shifting to the input signal was created and tested using DVB-S2 satellite modems. Testing not only verified the functionality of the emulator but also showed that DVB-S2 modems are unequipped to handle the continuous spectral frequency shifts due to the Doppler effect. The emulator framework has considerable room for growth, both in terms of implementing new channel transformation models as well as the re-implementation of the emulator on custom hardware for emulation of channels with wider bandwidths, more complex noise sources, or platform-dependent spatial blockage effects.
by Joseph William Colosimo.
M. Eng.
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Walther, Julia, Lars Kirsten, and Edmund Koch. "Optimal processing of Doppler signals in OCT." SPIE, 2015. https://tud.qucosa.de/id/qucosa%3A35190.

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Besides structural imaging, OCT can be used to estimate axial velocities of the sample resolved in depth by Dopplerprocessing. In Fourier domain OCT (FD-OCT), this is accomplished by measuring the phase difference (i.e. phase shift) between timely separated A-scans at the same depth. In most cases, these data are disturbed by noise caused by intrinsic noise of the OCT system, specified by the SNR, and decorrelation noise caused by the transversal movement of the optical beam relative to the sample. Since the first use of Doppler methods in OCT, many methods to reduce the phase shift noise by averaging have been presented. While all these methods use a fixed set of consecutive A-scans, the best method, exhibiting no bias and having the smallest standard deviation, was questionable. Recently, Doppler processing methods depending on the mentioned noise sources and delivering the most likely phase shift and thereby axial velocity became available. The relation of these methods to previously known methods like the Kasai estimator, maximum likelihood estimator (MLE) and joint spectral and time domain OCT (jSTdOCT) will be discussed.
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Mehmood, Asif. "Instantaneous frequency analysis of ultrasonic doppler vibrometery signals /." Full text available from ProQuest UM Digital Dissertations, 2008. http://0-proquest.umi.com.umiss.lib.olemiss.edu/pqdweb?index=0&did=1850482451&SrchMode=1&sid=2&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1277394744&clientId=22256.

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Thesis (Ph.D.)--University of Mississippi, 2008.
Typescript. Vita. "May 2008." Dissertation director : Paul M. Goggans Includes bibliographical references (leaves 106-112). Also available online via ProQuest to authorized users.
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Eldred, Randy Michael. "Doppler processing of phase encoded underwater acoustic signals." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA241283.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 1990.
Thesis Advisor(s): Miller, James H. Second Reader: Tummala, Murali. "September 1990." Description based on title screen as viewed on December 17, 2009. DTIC Identifier(s): Acoustic tomography, inverse problems, Fast Hadamard Transforms, theses. Author(s) subject terms: Acoustic tomography, Fast Hadamard Transform, maximal-length sequences, Doppler processing. Includes bibliographical references (p. 95-96). Also available in print.
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Malachias, Nickolaos. "Doppler shift and spread study for ionospherically propagated signals." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA284610.

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Alzogaiby, Adel. "Using Micro-Doppler radar signals for human gait detection." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86652.

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Thesis (MScEng)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: This work entails the development and performance analysis of a human gait detection system based on radar micro-Doppler signals. The system consists of a tracking functionality and a target classifier. Target micro-Doppler signatures are extracted with Short-Time Fourier Transform (STFT) based spectrogram providing a high-resolution signatures with the radar that is used. A feature extraction mechanism is developed to extract six features from the signature and an artificial neural network (A-NN) based classifier is designed to carry out the classification process. The system is tested on real X-band radar data of human subjects performing six activities. Those activities are walking and speed walking, walking with hands in pockets, marching, running, walking with a weapon, and walking with arms swaying. The multiclass classifier was designed to discriminate between those activities. High classification accuracy of 96% is demonstrated.
AFRIKAANSE OPSOMMING: Hierdie werk behels die ontwikkeling, en analise van werksverrigting, van ’n menslike stapdetekor gebaseer op radar-mikrodoppleranalise. Die stelsel bestaan uit ’n teikenvolger en -klassifiseerder. Die mikrodoppler-kenmerke van ’n teiken word met behulp van die korttyd-Fourier-transform onttrek, en verskaf hoe-resolusie-kenmerke met die radar wat vir die implementering gebruik word. ’n Kenmerkontrekkingstelsel is ontwikkel om ses kenmerke vanuit die spektrogram te onttrek, en ’n kunsmatige neurale netwerk word as klassifiseerder gebruik. Die stelsel is met ’n X-band radar op werklike menslike beweging getoets, terwyl vrywilligers ses aktiwiteite uitgevoer het: loop, loop (hand in die sakke), marsjeer, hardloop, loop met ’n wapen, loop met arms wat swaai. Die multiklas-klassifiseerder is ontwerp om tussen hierdie aktiwiteite te onderskei. ’n Hoe klassifiseringsakkuraatheid van 96% word gedemonstreer.
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Fan, Lingke. "Spectral and time-frequency analysis of ultrasonic Doppler signals." Thesis, University of Leicester, 1994. http://hdl.handle.net/2381/34342.

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Spectral analysis of Doppler signals plays a very important role in non-invasive measurements of blood velocity distributions. Among the various spectral analysis methods available, the fast Fourier transform (FFT) is regarded as a "traditional" spectral analysis tool and is widely used in commercial, clinical, experimental and research equipment. Some drawbacks of this method, however, have imposed limitations on its use in some clinical cases. A numbers of spectral and time-frequency analysis methods have been studied in this dissertation. These include the traditional FFT, the autoregressive (AR) method, the Wigner-Ville distribution (WVD), and the Choi-Williams distribution (CWD). The advantages and disadvantages of each method have been studied and summarised. Efforts have been made to improve the temporal and frequency resolution of the results. New analysis methods such as the WVD and CWD have been interpreted physically, and some of their new properties have been explored. The results have suggested that the heights of the peaks in the AR spectra of narrow-band signals are not necessarily proportional to signal power, and should be used with caution in the context of Doppler signal processing. The results have also shown that it is appropriate to use the WVD or CWD to analyse signals when high temporal resolution is required. In practice, it is easier for the operator to handle the WVD, which usually produces reasonably good results. The above methods have been applied in practice. Considerable software and hardware development has been carried out, and a number of analysers have been implemented for use under different practical conditions. These analysers were also used to compare experimentally the analysis methods mentioned above, and to confirm the results of theoretical analyses. Some of these analysers have found applications in clinical practice.
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Jack, Susan Heather. "The investigation of underwater acoustic signals using Laser Doppler Anemometry." Thesis, University of Edinburgh, 2000. http://hdl.handle.net/1842/15088.

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Laser Doppler Anemometry (LDA) has been used to study underwater acoustic signals both from emitting hydrophones and underwater explosions. A dual-beam LDA arrangement was used to capture Doppler signals arising from light scattered from particles suspended at the point of interest in the flow. These Doppler signals are analysed using either Hilbert transforms or wavelets, both of which allow instantaneous frequency information to be obtained. When an acoustic signal propagates through a medium it creates refractive index variations within the medium. The apparent motion of the scattering particles, as observed by the detector, which give rise to the Doppler signal, is therefore made up of two components. Firstly, the particles oscillate due to the sound field and secondly, the interference fringes oscillate due to the refractive index variations. This is termed the acousto-optic effect. A theory has been developed to investigate the effect of these refractive index variations on the analysed Doppler signals of an LDA system. Analysis of experimental Doppler signals using the Hilbert transform technique shows close agreement with the theoretical predictions. LDA has also been used to investigate the acoustic signal emitted by an oscillating explosion bubble. This is generated by an underwater spark which creates a similar situation to an underwater explosion in which a shock wave and an oscillating bubble are produced. Analysis of the Doppler signal using wavelets provides information on the bubble period, radius, energy and particle velocity. Explosive materials have traditionally been used for investigation of underwater explosions but they have the disadvantage of obscuring the area with explosion debris thus making optical investigation difficult. It is shown in this work that the use of LDA and analysis of Doppler signals using wavelets is an accurate technique for the investigation of acoustic signals from underwater explosions. This allows investigation of the area close to the explosion centre where measurements have been difficult to achieve with traditional techniques.
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CIATTAGLIA, Gianluca. "Modern techniques to process micro-Doppler signals from mmWave Radars." Doctoral thesis, Università Politecnica delle Marche, 2022. http://hdl.handle.net/11566/295142.

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I sistemi radar mmWave stanno diventando molto comuni sui veicoli e le loro capacità, in termini di portata e velocità, li rendono adatti a un'altra classica applicazione radar classica, quella relativa all'effetto micro-Doppler. Dall'elaborazione dei segnali radar mmWave, l'effetto micro-Doppler può essere sfruttato, rendendo così possibile estrarre informazioni interessanti sui bersagli. Con l'enorme larghezza di banda e il breve tempo di trasmissione del segnale, l effetto micro-Doppler può essere utilizzato per diversi scopi come la vibrazione del bersaglio o la classificazione dei bersagli. Grazie anche al progresso delle tecniche di Machine Learning, la loro combinazione con elaborazione del segnale radar è un campo interessante da esplorare e può essere usato per fornire soluzioni a diversi problemi radar. L'effetto Micro-Doppler ha una lunga storia nei sistemi radar, un sacco di letteratura può essere trovata su questo argomento, ma la maggior parte di loro considera dispositivi non commerciali quindi è abbastanza lontano da un caso pratico. In questa dissertazione, diverse tecniche per elaborare i segnali micro-Doppler provenienti da radar automobilistici sarà presentato, con lo scopo di classificarli ed estrarre informazioni sulle vibrazioni dal bersaglio. Il contributo principale di questo lavoro è la proposta di nuove tecniche che possono essere applicato su un sensore commerciale e li rende adatti per il micro- Doppler.
mmWave Radar systems are becoming very common on vehicles and their capabilities, in terms of range and velocity, make them suitable for another classical radar application, the one related to the micro-Doppler effect. From the processing of mmWave radar signals, the micro-Doppler effect can be exploited, making so possible to extract interesting information on the observed targets. With the huge bandwidth and the short signal transmission time, the micro-Doppler effect can be used for different purposes such as target vibration measurements or targets classification. Thanks also to the advance of Machine Learning techniques, their combination with radar signal processing is an interesting field to explore and can be used to provide solutions to different radar problems. The Micro-Doppler effect has a long story in Radar systems, a lot of literature can be found on this topic but most of them consider non-commercial devices so is quite away from a practical case. In this dissertation, different techniques to process the micro-Doppler signals coming from automotive radars will be presented, with the purpose of classifying them and extracting vibration information from the target. The main contribution of this work is the proposal of novel techniques that can be applied on a commercial sensor and makes them suitable for the micro- Doppler application.
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Wendling, Fabrice. "Simulation of doppler ultrasound signals for a laminar, pulsatile, nonuniform flow." Thesis, Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/16875.

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Books on the topic "Doppler Signals"

1

Malachias, Nickolaos. Doppler shift and spread study for ionospherically propagated signals. Monterey, Calif: Naval Postgraduate School, 1994.

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Kharchakdjian, Raffi. Modelling the structure of the tumour vasculature and its effect on doppler ultrasound signals. Ottawa: National Library of Canada, 2001.

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N, McDicken W., ed. Doppler ultrasound: Physics, instrumentation, and signal processing. 2nd ed. Chichester: Wiley, 2000.

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Keenan, Desmond Barry. Enhanced signal processing of pulsed doppler ultrasound. [s.l: The Author], 1998.

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Eggen, Trym H. Underwater acoustic communication over Doppler spread channels. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1997.

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United States. National Aeronautics and Space Administration., ed. Windshear detection radar signal processing studies: Final report. Clemson, S.C: Electrical and Computer Engineering Dept., Clemson University, 1993.

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L, Weber B., and Environmental Technology Laboratory (Oceanic and Atmospheric Research Laboratories), eds. A new paradigm for Doppler radar wind profiler signal processing. Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Office of Oceanic and Atmospheric Research, Environmental Technology Laboratory, 2004.

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Zemli͡anskiĭ, V. M. Izmerenie skorosti potokov lazernym doplerovskim metodom: Raschet parametrov doplerovskogo signala LDA s uchetom poli͡arizat͡sionno-fazovykh ėffektov rassei͡anii͡a. Kiev: Gol. izd-vo izdatelʹskogo obʺedinenii͡a "Vyshcha shkola", 1987.

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United States. National Aeronautics and Space Administration., ed. Coherent Doppler Lidar signal covariance including wind shear and wind turbulence. [Washington, DC: National Aeronautics and Space Administration, 1993.

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Hepner, Timothy E. State-of-the-art laser Doppler velocimeter signal processors: calibration and evaluation. Washington, D. C: American Institute of Aeronautics and Astronautics, 1994.

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Book chapters on the topic "Doppler Signals"

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Georgiadis, Dimitrios, and Mario Siebler. "Detection of Microembolic Signals with Transcranial Doppler Ultrasound." In Handbook on Neurovascular Ultrasound, 194–205. Basel: KARGER, 2006. http://dx.doi.org/10.1159/000092401.

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Andersen, Knud, and Anders Høst-Madsen. "Quantization of Doppler Signals: how many bits are needed?" In Developments in Laser Techniques and Applications to Fluid Mechanics, 260–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-79965-5_17.

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Setlak, Lucjan, Rafał Kowalik, and Maciej Smolak. "Doppler Delay in Navigation Signals Received by GNSS Receivers." In Lecture Notes in Electrical Engineering, 3–8. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21507-1_1.

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Tworzydlo, P., and A. D. C. Chan. "Spectral Analysis of Respiratory and Cardiac Signals Using Doppler Radar." In IFMBE Proceedings, 1034–38. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19387-8_252.

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Brinkløv, Signe M. M., Lasse Jakobsen, and Lee A. Miller. "Echolocation in Bats, Odontocetes, Birds, and Insectivores." In Exploring Animal Behavior Through Sound: Volume 1, 419–57. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97540-1_12.

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AbstractIn this chapter, the authors review basic concepts about echolocation, the variety of animals known to echolocate, the production of echolocation signals, the different types of echolocation signals, the hearing anatomy, and how echolocating animals use echolocation. The differences between echolocation signals in air versus water are discussed. Echolocation abilities have been studied intensively in bats and toothed whales, the two groups with the most sophisticated echolocation systems in terms of physiological specializations and performance. Echolocation has also been documented in oilbirds and swiftlets; and a crude form of echo-based orientation may be present in tenrecs and shrews.The authors emphasize that the ability to produce ultrasonic sounds does not necessarily imply an echolocation function. Most echolocators (i.e., a select group of bats, toothed whales, oilbirds, and swiftlets) use broadband clicks, but the majority of bats produce tonal echolocation signals of constant frequency, frequency modulation, or a combination of both. Most echolocators cannot broadcast and receive echolocation signals at the same time but separate each outgoing pulse from its returning echoes in time to detect the echoes and avoid masking caused by overlap with the outgoing signal. However, three families of bats can tolerate pulse-echo overlap and use the Doppler shift to identify prey items.A primary advantage of echolocation is allowing animals to operate and orient independently of ambient light conditions. At the same time, information leakage is a primary disadvantage of echolocation. The signals used in echolocation are audible to many other animals, such as competing conspecifics, predators, and prey.
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Su, Zhe, Lixin Zhang, Xiaoping Qian, Qibing Xu, and Yisong Li. "Bispectra-Mellin Transform and Its Application in Doppler-Distorted Pulsar Signals." In Lecture Notes in Electrical Engineering, 627–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29187-6_61.

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Flood, Gabrielle, Anders Heyden, and Kalle Åström. "Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals." In Lecture Notes in Computer Science, 116–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05499-1_7.

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Domnick, J., H. Ertel, and C. Tropea. "Processing of Phase-Doppler Signals Using the Cross-Spectral Density Function." In Applications of Laser Anemometry to Fluid Mechanics, 473–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-83844-6_26.

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Zhang, Yufeng, Le Wang, Yali Gao, Jianhua Chen, and Xinling Shi. "Automatic De-noising of Doppler Ultrasound Signals Using Matching Pursuit Method." In Independent Component Analysis and Blind Signal Separation, 519–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11679363_65.

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Agostini, L., A. Fort, C. Manfredi, L. Masotti, F. Picchiarini, and S. Rocchi. "Autoregressive Recursive Algorithms for Maximum Frequency Estimate of Doppler Ultrasonic Signals." In Acoustical Imaging, 479–90. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1943-0_51.

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Conference papers on the topic "Doppler Signals"

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Dabas, A., and P. H. Flamant. "Spectral and Covariance algorithms for Doppler Lidar Signals." In Coherent Laser Radar. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/clr.1991.thd1.

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The performances in terms of bias and standard deviation of the frequency estimators originally applied to Doppler Radar signals (FFT, Pulse-Pair and Poly-Pulse-Pair), are analysed, when working on Doppler Lidar signals characterized by low Signal to Noise Ratio (down to −10dB). Analytical equations were presented by Berger&Groginsky, and Zrnic, for the statistical error as a function of SNR and signal processor parameters. Those equations are rederived from the basic maths to make more explicit the various assumptions and limitations they imply. Those equations are then systematically compared to numerically derived performances (study made for the franco-german airbone Doppler lidar project WIND), based on simulated Doppler Lidar signals. Examples are presented of the estimator standard deviations when they are applied to signals with wide-band SNR ranging from −10dB to +10dB.
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Babu, T. P. Sameer, and P. Murali Krishna. "High resolution Doppler estimation using highly Doppler tolerant signals." In 2009 International Symposium on Ocean Electronics (SYMPOL 2009). IEEE, 2009. http://dx.doi.org/10.1109/sympol.2009.5664133.

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Toth, Arthur R., Kaustubh Kalgaonkar, Bhiksha Raj, and Tony Ezzat. "Synthesizing speech from Doppler signals." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495552.

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Shutko, Volodymyr, Olena Kolganova, Iuliia Silantieva, Lidiia Tereshchenko, and Mykola Shutko. "The Doppler Radar Signals Processing." In 2019 9th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2019. http://dx.doi.org/10.1109/acitt.2019.8779980.

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Xu, Shengzhi, and Alexander Yarovoy. "Doppler Shifts Mitigation for PMCW Signals." In 2019 International Radar Conference (RADAR). IEEE, 2019. http://dx.doi.org/10.1109/radar41533.2019.171290.

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Franken, G. E. A., H. Nikookar, and P. Genderen. "Doppler Tolerance of OFDM-coded Radar Signals." In 2006 European Radar Conference. IEEE, 2006. http://dx.doi.org/10.1109/eurad.2006.280285.

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Walther, Julia, Lars Kirsten, and Edmund Koch. "Optimal processing of Doppler signals in OCT." In European Conference on Biomedical Optics. Washington, D.C.: OSA, 2015. http://dx.doi.org/10.1364/ecbo.2015.954102.

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Campos, Rita, Edite Figueiras, Luis F. Requicha Ferreira, and Anne Humeau-Heurtier. "Spectral analysis of laser Doppler flowmetry signals." In 2012 IEEE 2nd Portuguese Meeting in Bioengineering (ENBENG). IEEE, 2012. http://dx.doi.org/10.1109/enbeng.2012.6331342.

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Klochko, Vladimir K., and Irina V. Andreeva. "Signals Processing in Doppler Medical Measurement System." In 2023 25th International Conference on Digital Signal Processing and its Applications (DSPA). IEEE, 2023. http://dx.doi.org/10.1109/dspa57594.2023.10113435.

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Andric, Milenko S., Dimitrije M. Bujakovic, Boban P. Bondzulic, and Bojan M. Zrnic. "Cepstrum-based analysis of radar Doppler signals." In TELSIKS 2011 - 2011 10th International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services. IEEE, 2011. http://dx.doi.org/10.1109/telsks.2011.6143180.

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Reports on the topic "Doppler Signals"

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Cayse, Robert W. Application of the Hilbert Transform to Doppler Radar Signals from a Hypervelocity Gun. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada193690.

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Lohman, B., O. Boric-Lubecke, V. M. Lubecke, P. W. Ong, and M. M. Sondhi. A Digital Signal Processor for Doppler Radar Sensing of Vital Signs. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada412597.

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