Journal articles: 'Detection and estimation theory'

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Noam, Yair, and Joseph Tabrikian. "Marginal Likelihood for Estimation and Detection Theory." IEEE Transactions on Signal Processing 55, no. 8 (August 2007): 3963–74. http://dx.doi.org/10.1109/tsp.2007.894411.

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Baggeroer, Arthur B. "Inverse methods and detection and estimation theory." Journal of the Acoustical Society of America 89, no. 4B (April 1991): 1873. http://dx.doi.org/10.1121/1.2029337.

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Wang, Feng, Mei Quan Liu, and Jiang Wei Fan. "The Performance Correlation Hilbert Time-Delay Estimation for Passive Detection." Applied Mechanics and Materials 602-605 (August 2014): 1768–71. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1768.

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Passive time difference detection method is distance, high speed and good concealment which has broad military application prospects. One of the key technologies for passive detection is to extract the time lag through effective signal processing. Relevant method is the most basic method to estimate the time difference and is the basic theory of all correlative time-delay estimation algorithms. The method is simple. But good results rely on the spectrum characteristics of signal and noise is ideal. Time delay estimation based on Hilbert transform is the expansion of the generalized correlation time-delay estimation method which changes the correlation function from accidentally symmetry into odd symmetry. Detecting correlation peak is converted into zero crossing detection. The method sharps the main peak value point and improved the precision of time delay estimation which gets better time-delay estimation performance in the narrowband signal.

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Baggeroer, A. B., W. A. Kuperman, and H. Schmidt. "Matched field processing: A detection and estimation theory overview." Journal of the Acoustical Society of America 81, S1 (May 1987): S63. http://dx.doi.org/10.1121/1.2024326.

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Wu, Qiuyuan, Jiping Qiao, and Jinmian Wang. "Data anomaly detection based on improved kernel density estimation." Journal of Physics: Conference Series 2290, no. 1 (June 1, 2022): 012111. http://dx.doi.org/10.1088/1742-6596/2290/1/012111.

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Abstract With the advent of the era of big data, while machines process data instead of humans, timely detection of abnormal conditions in data through algorithms has become the focus of research, and the importance of data detection accuracy has also increased. Therefore, based on the traditional kernel density estimation theory, this paper adds adaptive diffusion equation theory to improve it, selects the best window width value to achieve a high degree of data distribution fitting, and further formulates detection rules. Finally, compared with other traditional detection methods, the results show that the method proposed in this paper can improve the accuracy and stability of abnormal data detection.

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Wang, Lujun, and Zhaoning Zheng. "Joint detection and estimation theory in underwater acoustics signal processing." Acoustical Science and Technology 25, no. 1 (2004): 70–72. http://dx.doi.org/10.1250/ast.25.70.

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Fonollosa, Jordi, Alexander Vergara, Ramón Huerta, and Santiago Marco. "Estimation of the limit of detection using information theory measures." Analytica Chimica Acta 810 (January 2014): 1–9. http://dx.doi.org/10.1016/j.aca.2013.10.030.

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He, Xing, Lei Chu, Robert Caiming Qiu, Qian Ai, Zenan Ling, and Jian Zhang. "Invisible Units Detection and Estimation Based on Random Matrix Theory." IEEE Transactions on Power Systems 35, no. 3 (May 2020): 1846–55. http://dx.doi.org/10.1109/tpwrs.2019.2935739.

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Fukuhara, Hirotaka, and Akihito Kamata. "A Bifactor Multidimensional Item Response Theory Model for Differential Item Functioning Analysis on Testlet-Based Items." Applied Psychological Measurement 35, no. 8 (November 2011): 604–22. http://dx.doi.org/10.1177/0146621611428447.

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A differential item functioning (DIF) detection method for testlet-based data was proposed and evaluated in this study. The proposed DIF model is an extension of a bifactor multidimensional item response theory (MIRT) model for testlets. Unlike traditional item response theory (IRT) DIF models, the proposed model takes testlet effects into account, thus estimating DIF magnitude appropriately when a test is composed of testlets. A fully Bayesian estimation method was adopted for parameter estimation. The recovery of parameters was evaluated for the proposed DIF model. Simulation results revealed that the proposed bifactor MIRT DIF model produced better estimates of DIF magnitude and higher DIF detection rates than the traditional IRT DIF model for all simulation conditions. A real data analysis was also conducted by applying the proposed DIF model to a statewide reading assessment data set.

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Dehling, H., R. Fried, and M. Wendler. "A robust method for shift detection in time series." Biometrika 107, no. 3 (March 14, 2020): 647–60. http://dx.doi.org/10.1093/biomet/asaa004.

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Summary We present a robust and nonparametric test for the presence of a changepoint in a time series, based on the two-sample Hodges–Lehmann estimator. We develop new limit theory for a class of statistics based on two-sample U-quantile processes in the case of short-range dependent observations. Using this theory, we derive the asymptotic distribution of our test statistic under the null hypothesis of a constant level. The proposed test shows better overall performance under normal, heavy-tailed and skewed distributions than several other modifications of the popular cumulative sums test based on U-statistics, one-sample U-quantiles or M-estimation. The new theory does not involve moment conditions, so any transform of the observed process can be used to test the stability of higher-order characteristics such as variability, skewness and kurtosis.

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Wang, Song, and Zengfu Wang. "Optical Flow Estimation with Occlusion Detection." Algorithms 12, no. 5 (May 1, 2019): 92. http://dx.doi.org/10.3390/a12050092.

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The dense optical flow estimation under occlusion is a challenging task. Occlusion may result in ambiguity in optical flow estimation, while accurate occlusion detection can reduce the error. In this paper, we propose a robust optical flow estimation algorithm with reliable occlusion detection. Firstly, the occlusion areas in successive video frames are detected by integrating various information from multiple sources including feature matching, motion edges, warped images and occlusion consistency. Then optimization function with occlusion coefficient and selective region smoothing are used to obtain the optical flow estimation of the non-occlusion areas and occlusion areas respectively. Experimental results show that the algorithm proposed in this paper is an effective algorithm for dense optical flow estimation.

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Chen, Wen, Afef Fekih, and Zehui Mao. "Fault Detection, Estimation/Reconstruction, and Fault-Tolerant Control: Theory and Practice." Mathematical Problems in Engineering 2016 (2016): 1–2. http://dx.doi.org/10.1155/2016/5293171.

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Wang, Xin, Zhengyuan Zhu, and Hao Helen Zhang. "Spatial heterogeneity automatic detection and estimation." Computational Statistics & Data Analysis 180 (April 2023): 107667. http://dx.doi.org/10.1016/j.csda.2022.107667.

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Bougharriou, Sana, Fayçal Hamdaoui, and Abdellatif Mtibaa. "Vehicles distance estimation using detection of vanishing point." Engineering Computations 36, no. 9 (November 11, 2019): 3070–93. http://dx.doi.org/10.1108/ec-09-2018-0416.

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Purpose This paper aims to study distance determination in vehicles, which could allow an in-car system to provide feedback and alert drivers, by either prompting the driver to take preventative action or prepare the vehicle’s safety systems for an imminent collision. The success of a new system's deploying allows drivers to oppose the huge number of accidents and the material losses and costs associated with car accidents. Design/methodology/approach In this context, this paper presents estimation distance between camera and frontal vehicles based on camera calibration by combining three main steps: vanishing point extraction, lanes detection and vehicles detection in the field of 3 D real scene. This algorithm was implemented in MATLAB, and it was applied on scenes containing several vehicles in highway urban area. The method starts with the camera calibration. Then, the distance information can be calculated. Findings Based on experiment performance, this new method achieves robustness especially for detecting and estimating distances for multiple vehicles in a single scene. Also, this method demonstrates a higher accuracy detection rate of 0.869 in an execution time of 2.382 ms. Originality/value The novelty of the proposed method consists firstly on the use of an adaptive segmentation to reject the false points of interests. Secondly, the use of vanishing point has reduced the cost of using memory. Indeed, the part of the image above the vanishing point will not be processed and therefore will be deleted. The last benefit is the application of this new method on structured roads.

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Dai, Enze, Duan Huang, and Ling Zhang. "Low-Rate Denial-of-Service Attack Detection: Defense Strategy Based on Spectral Estimation for CV-QKD." Photonics 9, no. 6 (May 24, 2022): 365. http://dx.doi.org/10.3390/photonics9060365.

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Although continuous-variable quantum key distribution (CVQKD) systems have unconditional security in theory, there are still many cyber attacking strategies proposed that exploit the loopholes of hardware devices and algorithms. At present, few studies have focused on attacks using algorithm vulnerabilities. The low-rate denial-of-service (LDoS) attack is precisely an algorithm-loophole based hacking strategy, which attacks by manipulating a channel’s transmittance T. In this paper, we take advantage of the feature that the power spectral density (PSD) of LDoS attacks in low frequency band is higher than normal traffic’s to detect whether there are LDoS attacks. We put forward a detection method based on the Bartlett spectral estimation approach and discuss its feasibility from two aspects, the estimation consistency and the detection accuracy. Our experiment results demonstrate that the method can effectively detect LDoS attacks and maintain the consistency of estimation. In addition, compared with the traditional method based on the wavelet transform and Hurst index estimations, our method has higher detection accuracy and stronger pertinence. We anticipate our method may provide an insight into how to detect an LDoS attack in a CVQKD system.

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Mohammed, Sarah Sabah, and Maher K. Mahmood Al-Azawi. "Performance comparison of some weak signal detection techniques." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (May 1, 2022): 732. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp732-742.

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Performance comparison of some weak signal detection techniques is introduced. This comparison is very necessary since different applications require different operating conditions such as signal to noise ratio SNR, bandwidth, coherency, processing time and complexity. Three methods for detecting weak signals are considered. These are based on chaos theory, wavelet transform, and stocastic resonance. A detection algorithm based on a rectangular region in phase space plane is suggested in chaos method. The stocastic resonance method is considered in this research, as it is used for signal detection in underwater at a certain frequency. Simulation results obtained from MATLAB programs verify the studied methods giving an estimation of probability of detection and probability of false alarm versus SNR.

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Zhang, Miaoying, Fan Xiao, Rui Shao, and Zhaojun Deng. "Robust Fault Detection for Permanent-Magnet Synchronous Motor via Adaptive Sliding-Mode Observer." Mathematical Problems in Engineering 2020 (April 30, 2020): 1–6. http://dx.doi.org/10.1155/2020/9360939.

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In this paper, a robust fault detection problem is investigated for a permanent magnet synchronous motor (PMSM). By using the adaptive control and the sliding-mode control strategies, an observer is presented for estimating the amplitude demagnetization fault problem under inductance disturbance. The proposed method can effectively attenuate the inductance disturbance of the PMSM by the sliding-mode strategy. And the adaptive control estimation algorithm is adopted for guaranteeing that the real-time detection of demagnetization flux can be realized. The convergency is obtained by the Lyapunov stability theory. Finally, simulation is given for demonstrating the feasibility and effectiveness of the proposed method.

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Cohen, Dale J., and Len Lecci. "Using magnitude estimation to investigate the perceptual components of signal detection theory." Psychonomic Bulletin & Review 8, no. 2 (June 2001): 284–93. http://dx.doi.org/10.3758/bf03196163.

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Huang, Sijia, Qingzhi Du, Hua Long, and Yubin Shao. "Weak Signal Detection and Carrier Frequency Estimation Based on Cyclic Spectrum Theory." Procedia Computer Science 166 (2020): 62–67. http://dx.doi.org/10.1016/j.procs.2020.02.014.

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Ivanov, A. V., and I. V. Orlovskyi. "Detection of hidden periodicities in models with discrete time and long range dependent random noise." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 1 (2023): 48–58. http://dx.doi.org/10.17721/1812-5409.2023/1.7.

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Trigonometric regression models take a special place among various models of nonlinear regression analysis and signal processing theory. The problem of estimating the parameters of such models is called the problem of detecting hidden periodicities, and it has many applications in natural and technical sciences. The paper is devoted to the study of the problem of detecting hidden periodicities in the case when we observe only one harmonic oscillation with discrete time, where random noise is a local functional of Gaussian random sequence with singular spectrum. In particular, the random sequence in the model can be strongly dependent. For estimation of unknown parameters the periodogram estimator is chosen. Sufficient conditions of the consistency of the amplitude and angular frequency periodogram estimator of the model described above are obtained in the paper. The proof of Lemmas 1 and 2 gave an important asymptotic properties of the random noise functional related to the periodogram estimator which necessary for the proof of the main results. Series expansion of random noise in terms of Hermite polynomials and the Diagram formula are main tools that were used to obtain this lemmas.

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Vida, Denis, Peter S. Gural, Peter G. Brown, Margaret Campbell-Brown, and Paul Wiegert. "Estimating trajectories of meteors: an observational Monte Carlo approach – I. Theory." Monthly Notices of the Royal Astronomical Society 491, no. 2 (November 15, 2019): 2688–705. http://dx.doi.org/10.1093/mnras/stz3160.

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ABSTRACT It has recently been shown by Egal et al. that some types of existing meteor in-atmosphere trajectory estimation methods may be less accurate than others, particularly when applied to high-precision optical measurements. The comparative performance of trajectory solution methods has previously only been examined for a small number of cases. Besides the radiant, orbital accuracy depends on the estimation of pre-atmosphere velocities, which have both random and systematic biases. Thus, it is critical to understand the uncertainty in velocity measurement inherent to each trajectory estimation method. In this first of a series of two papers, we introduce a novel meteor trajectory estimation method that uses the observed dynamics of meteors across stations as a global optimization function and that does not require either a theoretical or an empirical flight model to solve for velocity. We also develop a 3D observational meteor trajectory simulator that uses a meteor ablation model to replicate the dynamics of meteoroid flight, as a means to validate different trajectory solvers. We both test this new method and compare it to other methods, using synthetic meteors from three major showers spanning a wide range of velocities and geometries (Draconids, Geminids, and Perseids). We determine which meteor trajectory solving algorithm performs better for all-sky, moderate field-of-view, and high-precision narrow-field optical meteor detection systems. The results are presented in the second paper in this series. Finally, we give detailed equations for estimating meteor trajectories and analytically computing meteoroid orbits, and provide the python code of the methodology as open-source software.

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Chen, Hai Bin, Nan Ge, and Xiao Jun Tong. "Research on the Confidence Interval of Indirect Detection." Advanced Materials Research 418-420 (December 2011): 532–35. http://dx.doi.org/10.4028/www.scientific.net/amr.418-420.532.

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Abstract. Using the correlation between the measure value and measured value in the indirect detection, the whole presumption method and theoretical formula of the confidence intervals for measured value are put forward. Based on the different detection methods, the confidence interval of high confidence and high accuracy can be given by the proposed method according to random measurement results. Through the Monte Carlo simulation, using the deducing method and the related theory, it may be concluded that the true value is included within the confidence interval which is obtained by this method. The traditional method can only get the point estimation but not give the confidence intervals in the practical engineering. According to the method, the interval estimation of concrete strength can be give. Moreover, this method is used not only in test concrete strength, especially in the evaluation of earthquake, but also in strength detecting for bridges, the pressure vessel, aircraft wing etc.

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Syed Musa, Syed Mohamad Sadiq, Mohd Salmi Md Noorani, Fatimah Abdul Razak, Munira Ismail, Mohd Almie Alias, and Saiful Izzuan Hussain. "An Early Warning System for Flood Detection Using Critical Slowing Down." International Journal of Environmental Research and Public Health 17, no. 17 (August 24, 2020): 6131. http://dx.doi.org/10.3390/ijerph17176131.

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The theory of critical slowing down (CSD) suggests an increasing pattern in the time series of CSD indicators near catastrophic events. This theory has been successfully used as a generic indicator of early warning signals in various fields, including climate research. In this paper, we present an application of CSD on water level data with the aim of producing an early warning signal for floods. To achieve this, we inspect the trend of CSD indicators using quantile estimation instead of using the standard method of Kendall’s tau rank correlation, which we found is inconsistent for our data set. For our flood early warning system (FLEWS), quantile estimation is used to provide thresholds to extract the dates associated with significant increases on the time series of the CSD indicators. We apply CSD theory on water level data of Kelantan River and found that it is a reliable technique to produce a FLEWS as it demonstrates an increasing pattern near the flood events. We then apply quantile estimation on the time series of CSD indicators and we manage to establish an early warning signal for ten of the twelve flood events. The other two events are detected on the first day of the flood.

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Pan, Wenbo, Xianghua Fan, Hongbo Li, and Kai He. "Long-Range Perception System for Road Boundaries and Objects Detection in Trains." Remote Sensing 15, no. 14 (July 10, 2023): 3473. http://dx.doi.org/10.3390/rs15143473.

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This article introduces a long-range sensing system based on millimeter-wave radar, which is used to detect the roadside boundaries and track trains for trains. Due to the high speed and long braking distance of trains, existing commercial vehicle sensing solutions cannot meet their needs for long-range target detection. To address this challenge, this study proposes a long-range perception system for detecting road boundaries and trains based on millimeter-wave radar. The system uses high-resolution, long-range millimeter-wave radar customized for the strong scattering environment of rail transit. First, we established a multipath scattering theory in complex scenes such as track tunnels and fences and used the azimuth scattering characteristics to eliminate false detections. A set of accurate calculation methods of the train’s ego-velocity is proposed, which divides the radar detection point clouds into static target point clouds and dynamic target point clouds based on the ego-velocity of the train. We then used the road boundary curvature, global geometric parallel information, and multi-frame information fusion to extract and fit the boundary in the static target point stably. Finally, we performed clustering and shape estimation on the radar track information to identify the train and judge the collision risk based on the position and speed of the detected train and the extracted boundary information. The paper makes a significant contribution by establishing a multipath scattering theory for complex scenes of rail transit to eliminate radar false detection and proposing a train speed estimation strategy and a road boundary feature point extraction method that adapt to the rail environment. As well as building a perception system and installing it on the train for verification, the main line test results showed that the system can reliably detect the road boundary more than 400 m ahead of the train and can stably detect and track the train.

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Shi, Juan, Qunfei Zhang, Weijie Tan, Linlin Mao, Lihuan Huang, and Wentao Shi. "Underdetermined DOA Estimation for Wideband Signals via Focused Atomic Norm Minimization." Entropy 22, no. 3 (March 20, 2020): 359. http://dx.doi.org/10.3390/e22030359.

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In underwater acoustic signal processing, direction of arrival (DOA) estimation can provide important information for target tracking and localization. To address underdetermined wideband signal processing in underwater passive detection system, this paper proposes a novel underdetermined wideband DOA estimation method equipped with the nested array (NA) using focused atomic norm minimization (ANM), where the signal source number detection is accomplished by information theory criteria. In the proposed DOA estimation method, especially, after vectoring the covariance matrix of each frequency bin, each corresponding obtained vector is focused into the predefined frequency bin by focused matrix. Then, the collected averaged vector is considered as virtual array model, whose steering vector exhibits the Vandermonde structure in terms of the obtained virtual array geometries. Further, the new covariance matrix is recovered based on ANM by semi-definite programming (SDP), which utilizes the information of the Toeplitz structure. Finally, the Root-MUSIC algorithm is applied to estimate the DOAs. Simulation results show that the proposed method outperforms other underdetermined DOA estimation methods based on information theory in term of higher estimation accuracy.

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Masoud, Mohammad Z., Yousef Jaradat, Ismael Jannoud, and Mustafa A. Al Sibahee. "A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network." International Journal of Distributed Sensor Networks 15, no. 6 (June 2019): 155014771985823. http://dx.doi.org/10.1177/1550147719858231.

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In this work, a new hybrid clustering routing protocol is proposed to prolong network life time through detecting holes and edges nodes. The detection process attempts to generate a connected graph without any isolated nodes or clusters that have no connection with the sink node. To this end, soft clustering/estimation maximization with graph metrics, PageRank, node degree, and local cluster coefficient, has been utilized. Holes and edges detection process is performed by the sink node to reduce energy consumption of wireless sensor network nodes. The clustering process is dynamic among sensor nodes. Hybrid clustering routing protocol–hole detection converts the network into a number of rings to overcome transmission distances. We compared hybrid clustering routing protocol–hole detection with four different protocols. The accuracy of detection reached 98%. Moreover, network life time has prolonged 10%. Finally, hybrid clustering routing protocol–hole detection has eliminated the disconnectivity in the network for more than 80% of network life time.

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Liu, Hai Tao, Dan Shao, and Bao Qiang Li. "Theory Analysis of Thermocouple Temperature Measurement." Applied Mechanics and Materials 239-240 (December 2012): 749–53. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.749.

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The thermocouple is extremely wide application in temperature measurement. The active sensor has the advantages of simple structure, wide measuring range, high precision, signal stability and other advantages and is widely used in the detection of furnace and pipeline temperature. The author analyzes the theory of measurement of thermocouple, and put forward a kind of originality of thought. According to this idea, the author derives several laws of thermocouple, and finds that a simple estimation method. Finally, some of the common thermocouple method measurement results are given.

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Auh, Geun-Sun. "On-Line Performance Monitoring With Signal Validation and Uncertainty Estimation." Journal of Engineering for Gas Turbines and Power 111, no. 4 (October 1, 1989): 694–96. http://dx.doi.org/10.1115/1.3240314.

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Methods are developed for performance monitoring of power plant components. On-line uncertainty estimation algorithms are developed. Since this can give new information about the validity of the measurements, proper use of performance monitoring can be achieved. A sequential fault detection method is introduced for the detection of small faults. This signal validation program gives an additional check for good inputs to the performance monitoring. Low-order models are solved using optimal estimation theory to get analytic measurements. The above algorithms are applied to heat transfer loops with real measurement data.

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Wang, Yan Li. "Interference Suppression Detection Algorithms in STBC-MIMO Systems." Applied Mechanics and Materials 130-134 (October 2011): 2940–43. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2940.

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Aiming at the uplink multi-user STBC-MIMO system, we adopted the combination of the detection scheme about the linear MMSE interference suppression and symbol estimation, and the receiver maintained decoding simplicity. Theory analysis and simulation results reveal that the proposed scheme can suppress and cancel the interference from co-channel users effectively without increasing the complexity significantly.

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Olivier, J. C., W. Kleynhans, and S. Miteff. "Teaching the Theory of Estimation and Detection via a GSM Radio Interface Simulation." IEEE Transactions on Education 49, no. 1 (February 2006): 61–66. http://dx.doi.org/10.1109/te.2005.856153.

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Zhang, Tengfei, and Huijuan Kang. "An Algorithm for Motion Estimation Based on the Interframe Difference Detection Function Model." Complexity 2021 (February 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/6638792.

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In this paper, we simulate the estimation of motion through an interframe difference detection function model and investigate the spatial-temporal context information correlation filtering target tracking algorithm, which is complex and computationally intensive. The basic theory of spatiotemporal context information and correlation filtering is studied to construct a fast target tracking method. The different computational schemes are designed for the flow of multiframe target detection from background removal to noise reduction, to single-frame detection, and finally to multiframe detection, respectively. This enables the ground-based telescope to effectively detect spatial targets in dense stellar backgrounds in both modes. The method is validated by simulations and experiments and can meet the requirements of real projects. The interframe bit attitude estimation is optimized by using the beam-parity method to reduce the interframe estimation noise; a global optimization strategy based on the bit attitude map is used in the back end to reduce the system computation amount and make the global bit attitude estimation more accurate; a loop detection based on the word pocket model is added to the system to reduce the cumulative error.

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Connolly, Francis T., and Giorgio Rizzoni. "Real Time Estimation of Engine Torque for the Detection of Engine Misfires." Journal of Dynamic Systems, Measurement, and Control 116, no. 4 (December 1, 1994): 675–86. http://dx.doi.org/10.1115/1.2899267.

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The need for improvements in the on-line estimation of engine performance variables is greater nowadays as a result of more stringent emission control legislation. There is also a concurrent requirement for improved on-board diagnostics to detect different types of malfunctions. For example, recent California Air Resources Board (CARB) regulations mandate continuous monitoring of misfires, a problem which, short of an expensive measurement of combustion pressure in each cylinder, is most directly approached by estimating individual cylinder torque. This paper describes the theory and experimental results of a method for the estimation of individual cylinder torque in automative engines, with the intent of satisfying the CARB misfire detection requirements. Estimation, control, and diagnostic functions associated with automotive engines involve near periodic processes, due to the nature of multi-cylinder engines. The model of the engine dynamics used in this study fully exploits the inherent periodicity of the combustion process in the crank angle domain in order to obtain a simple deconvolution method for the estimation of the mean torque produced by each cylinder during each stroke from a measurement of crankshaft angular velocity. The deconvolution is actually performed in the spatial frequency domain, recognizing that the combustion energy is concentrated at discrete spatial frequencies, which are harmonics of the frequency of rotation of the crankshaft. Thus, the resulting deconvolution algorithm is independent of engine speed, and reduces to an algebraic operation in the frequency domain. It is necessary to perform a Discrete Fourier Transform (DFT) on the measured angular velocity signal, sampled at fixed uniform crank angle intervals. The paper discusses the model used in the study, and the experimental validation of the algorithm, which has been implemented in real time using a portable computer and has been tested extensively on different production vehicles on a chassis dynamometer and on the road.

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Ma, Ning, and Jian Xin Wang. "Wideband LFM Signal Parameter Estimation Based on Compressed Sensing Theory." Applied Mechanics and Materials 548-549 (April 2014): 1160–65. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1160.

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Compressed sensing (CS) theory breaks through the limitations of the traditional Nyquist sampling theorem, and accomplishes the compressed sampling and reconstruction of signals based on sparsity or compressibility. In this paper, CS theory is used to do the parameter estimation of wideband Linear Frequency Modulated (LFM) signal in order to decrease the sampling pressure. A novel method that reconstructs the edge information of the LFM spectrum based on wavelet transform and CS theory is proposed. On the basis that the wideband LFM signal has approximate rectangular spectrum, the wavelet-based edge detection is introduced to provide sparse representation for the signal spectrum. The edges of the spectrum can be reconstructed by the CS reconstruction algorithms. Consequently, the initial frequency and final frequency of wideband LFM signal can be estimated with high estimation precision. The effectiveness of the proposed method is confirmed with numerical simulation.

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Villegas, Arturo, and Juan P. Torres. "Spatial spectroscopy for high resolution imaging." EPJ Web of Conferences 238 (2020): 06005. http://dx.doi.org/10.1051/epjconf/202023806005.

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Quantum estimation theory provides bounds for the precision in the estimation of a set of parameters that characterize a system. Two questions naturally arise: Is any of these bounds tight? And if this is the case, what type of measurements can attain such a limit? In this work we show that for phase objects, it is possible to find a tight resolution bound. Moreover one can find a set of spatial modes whose detection provides an optimal estimation of the complete set of parameters for which we propose a homodyne detection scheme. We call this method spatial spectroscopy since it mimics in the spatial domain what conventional spectroscopy methods do in the frequency domain employing many frequencies (hyperspectral imaging).

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Zhou, Xiaoli, Hongqiang Wang, Yongqiang Cheng, Yuliang Qin, and Haowen Chen. "Statistical Angular Resolution Limit for Ultrawideband MIMO Noise Radar." International Journal of Antennas and Propagation 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/906313.

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The two-dimensional angular resolution limit (ARL) of elevation and azimuth for MIMO radar with ultrawideband (UWB) noise waveforms is investigated using statistical resolution theory. First, the signal model of monostatic UWB MIMO noise radar is established in a 3D reference frame. Then, the statistical angular resolution limits (SARLs) of two closely spaced targets are derived using the detection-theoretic and estimation-theoretic approaches, respectively. The detection-theoretic approach is based on the generalized likelihood ratio test (GLRT) with given probabilities of false alarm and detection, while the estimation-theoretic approach is based on Smith’s criterion which involves the Cramér-Rao lower bound (CRLB). Furthermore, the relationship between the two approaches is presented, and the factors affecting the SARL, that is, detection parameters, transmit waveforms, array geometry, signal-to-noise ratio (SNR), and parameters of target (i.e., radar cross section (RCS) and direction), are analyzed. Compared with the conventional radar resolution theory defined by the ambiguity function, the SARL reflects the practical resolution ability of radar and can provide an optimization criterion for radar system design.

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Barbarossa, S. "Detection and imaging of moving objects with synthetic aperture radar. Part 1: Optimal detection and parameter estimation theory." IEE Proceedings F Radar and Signal Processing 139, no. 1 (1992): 79. http://dx.doi.org/10.1049/ip-f-2.1992.0010.

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Becciu, Alessandro, Andrea Fuster, Mark Pottek, Bart van den Heuvel, Bart ter Haar Romeny, and Hans van Assen. "3D Winding Number: Theory and Application to Medical Imaging." International Journal of Biomedical Imaging 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/516942.

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We develop a new formulation, mathematically elegant, to detect critical points of 3D scalar images. It is based on a topological number, which is the generalization to three dimensions of the 2D winding number. We illustrate our method by considering three different biomedical applications, namely, detection and counting of ovarian follicles and neuronal cells and estimation of cardiac motion from tagged MR images. Qualitative and quantitative evaluation emphasizes the reliability of the results.

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Yang, Baohua, Yuan Gao, Qian Yan, Lin Qi, Yue Zhu, and Bing Wang. "Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery." Sensors 20, no. 18 (September 4, 2020): 5021. http://dx.doi.org/10.3390/s20185021.

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Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder–random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R2 = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches.

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Ma, Jingxin, Haisen Li, Jianjun Zhu, and Baowei Chen. "Sound Velocity Estimation of Seabed Sediment Based on Parametric Array Sonar." Mathematical Problems in Engineering 2020 (August 14, 2020): 1–6. http://dx.doi.org/10.1155/2020/9810215.

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Backscattered sound waves of seabed sediments are important information carriers in seafloor detection and acoustic characteristic parameters inversion. Most of the existing methods for estimating geoacoustic parameters are based on multiangle seabed backscattered signal processing and suitable for flat seafloor conditions with uniform sediment thickness. This usually deviates from the real field conditions and affects the accuracy of parameter estimation. In this paper, the sound ray propagation theory is studied and analysed under the condition of sloping seabed and uneven sediment thickness. Based on the phased parameter array sonar system, a method of acoustic parameters estimation of the sediment under inclined seabed conditions is proposed. The simulation results show that the new method shows good adaptability to different inclination angles of the seabed and solves the problem of accuracy of acoustic parameter estimation of the inclined seabed sediments. The model will greatly reduce the seafloor topography requirements in the sediment acoustic parameter inversion, such as velocity, layer thickness, and acoustic impedance.

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Chung, Walter H., Jason L. Speyer, and Robert H. Chen. "A Decentralized Fault Detection Filter1." Journal of Dynamic Systems, Measurement, and Control 123, no. 2 (March 16, 1999): 237–47. http://dx.doi.org/10.1115/1.1367859.

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In this paper, we introduce the decentralized fault detection filter, a structure that results from merging decentralized estimation theory with the game theoretic fault detection filter. A decentralized approach may be the ideal way to health monitor large-scale systems, since it decomposes the problem down into (potentially smaller) “local” problems. These local results are then blended into a “global” result that describes the health of the entire system. The benefits of such an approach include added fault tolerance and easy scalability. An example given at the end of the paper demonstrates the use of this filter for a platoon of cars proposed for advanced vehicle control systems.

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Rekik, Wafa, Sylvie Le Hégarat-Mascle, Roger Reynaud, Abdelaziz Kallel, and Ahmed Ben Hamida. "Dynamic estimation of the discernment frame in belief function theory: Application to object detection." Information Sciences 306 (June 2015): 132–49. http://dx.doi.org/10.1016/j.ins.2015.01.039.

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Demarsin, Kris, Denis Vanderstraeten, Tim Volodine, and Dirk Roose. "Detection of closed sharp edges in point clouds using normal estimation and graph theory." Computer-Aided Design 39, no. 4 (April 2007): 276–83. http://dx.doi.org/10.1016/j.cad.2006.12.005.

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Chen, Jian Wei, You Po Su, and Hai Bin Chen. "Analytic Solution of Inverse Regression Equation for Evaluating Concrete Strength of Building Materials: Theory and Method." Applied Mechanics and Materials 357-360 (August 2013): 743–46. http://dx.doi.org/10.4028/www.scientific.net/amm.357-360.743.

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The interval estimation for the presumption of structure concrete strength should be gained because of strong randomness of test data. For further exaltation of accuracy and credibility test concrete strength, analytic solution of inverse regression equation for evaluating concrete strength of building materials is presented herein based on the inverse regression model. The accurate solution for the presumption of concrete strength can be obtained through the analytic solution, detection precision and reliability for the presumption of structure concrete strength can be improved with respect to the traditional point estimation method.

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Chen, Xiao, Yaan Li, Yuxing Li, and Jing Yu. "Active Sonar Target Tracking Based on the GM-CPHD Filter Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 4 (August 2018): 656–63. http://dx.doi.org/10.1051/jnwpu/20183640656.

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The estimation of underwater multi-target state has always been the difficult problem of active sonar target tracking.In order to get the variable number of target and their state, the random finite set theory is applied to multi-target tracking system.This theory not only effectively avoids the problem of multi-target tracking data association, and also realizes the estimation of time-varying number of targets and their states.Due to Probability Hypothesis Density(PHD) recursion propagates cardnality distribution with only a single parameter, a new generalization of the PHD recursion called Cardinalized Probability Hypothesis Density(CPHD) recursion, which jointly propagates the intensity function and the cardnality distribution, while have a big computation than PHD.Also there did not have closed-form solution for PHD recursion and CPHD recursion, so for linear Gaussian multi-target tracking system, the Gaussian Mixture Probability Hypothesis Density and Gaussian Mixture Cardinalized Probability Hypothesis Density(GM-CPHD) filter algorithm is put forward.GM-CPHD is more accurate than GM-PHD in estimation of the time-varying number of targets.In this paper, we use the ellipse gate tracking strategy to reduce computation in GM-CPHD filtering algorithm.At the same time, according to the characteristics of underwater target tracking, using active sonar equation, we get the relationship between detection probability, distance and false alarm, when fixed false alarm, analytic formula of the relationship between adaptive detection probability and distance is obtained, we puts forward the adaptive detection probability GM-CPHD filtering algorithm.Simulation shows that the combination of ellipse tracking gate strategy and adaptive detection probability GM-CPHD filtering algorithm can realize the estimation of the time-varying number of targets and their state more accuracy in dense clutter environment.

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Kroll, Martin. "Rate optimal estimation of quadratic functionals in inverse problems with partially unknown operator and application to testing problems." ESAIM: Probability and Statistics 23 (2019): 524–51. http://dx.doi.org/10.1051/ps/2018027.

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We consider the estimation of quadratic functionals in a Gaussian sequence model where the eigenvalues are supposed to be unknown and accessible through noisy observations only. Imposing smoothness assumptions both on the signal and the sequence of eigenvalues, we develop a minimax theory for this problem. We propose a truncated series estimator and show that it attains the optimal rate of convergence if the truncation parameter is chosen appropriately. Consequences for testing problems in inverse problems are equally discussed: in particular, the minimax rates of testing for signal detection and goodness-of-fit testing are derived.

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Wu, Yan Jun, Gang Fu, and Peng Yu. "Performance Analysis on Three Methods for Chirp Signal Parameters Estimation Based on FRFT." Advanced Materials Research 989-994 (July 2014): 3942–45. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3942.

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Chirp signal has been used widely in radar signals, and the Fractional Fourier transform is one of the most effective tools to analyze Chirp signal. In this paper, the concept of FRFT and the estimation theory of Chirp signal are introduced firstly. Then, we study three Chirp signal detection algorithms based on character of Chirp signal energy concentrated in a certain FRFT domain. Finally, in order to test the estimation abilities of the frequency modulation rate and the central frequency of FRFT to Chirp signal, and compare the operation time of parameters estimation under different SNR of the three algorithms, we simulate performance of the Three methods, and the final simulation results show that the three method have remarkable capabilities of detecting Chirp signal with low SNR. In contrast, the two-searching method doesn’t need planar search, consumedly reducing the computation cost at the same precision.

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Ferrat, S., and M. Crochet. "Methods of detection and estimation errors in ST radar studies." Annales Geophysicae 12, no. 6 (May 31, 1994): 489–96. http://dx.doi.org/10.1007/s00585-994-0489-2.

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Abstract. The classical theory of detection using the Neyman-Pearson principle is applied to stratosphere-troposphere (ST) radar signals. It is extended to provide information regarding the detection of weak signals which complements the detectability method usually employed in ST radar studies. It is shown that for ST radar signals of low amplitude and a detectability around 3 (a value commonly invoked in literature), the probability of detection is about equal to the probability of false alarm. The question of threshold detectability is also discussed. Spectral moments errors are evaluated by a method which is an extension of the analytical method of estimation developed by Miller and Rochwarger and the results compared to other statistical and analytical models. As already known, three factors can affect the error on the estimated parameters: the signal-to-noise ratio, the spectral width and the incoherent integration number. For high signal-to-noise ratios, analytical results are in good agreement with Barrick's and Denenberg's theoretical models and with Yamamoto's statistical one. For low signal-to-noise ratios, the spectral parameters are more sensitive to the selected model but overall variability is similar.

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Mohammadi, Shahriar, and Amin Namadchian. "A New Deep Learning Approach for Anomaly Base IDS using Memetic Classifier." International Journal of Computers Communications & Control 12, no. 5 (September 10, 2017): 677. http://dx.doi.org/10.15837/ijccc.2017.5.2972.

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A model of an intrusion-detection system capable of detecting attack in computer networks is described. The model is based on deep learning approach to learn best features of network connections and Memetic algorithm as final classifier for detection of abnormal traffic.One of the problems in intrusion detection systems is large scale of features. Which makes typical methods data mining method were ineffective in this area. Deep learning algorithms succeed in image and video mining which has high dimensionality of features. It seems to use them to solve the large scale of features problem of intrusion detection systems is possible. The model is offered in this paper which tries to use deep learning for detecting best features.An evaluation algorithm is used for produce final classifier that work well in multi density environments.We use NSL-KDD and Kdd99 dataset to evaluate our model, our findings showed 98.11 detection rate. NSL-KDD estimation shows the proposed model has succeeded to classify 92.72% R2L attack group.

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Shieh, Ming-Yuan, Chung-Yu Hsieh, and Tsung-Min Hsieh. "Fuzzy visual detection for human-robot interaction." Engineering Computations 31, no. 8 (October 28, 2014): 1709–19. http://dx.doi.org/10.1108/ec-11-2012-0292.

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Purpose – The purpose of this paper is to propose a fast object detection algorithm based on structural light analysis, which aims to detect and recognize human gesture and pose and then to conclude the respective commands for human-robot interaction control. Design/methodology/approach – In this paper, the human poses are estimated and analyzed by the proposed scheme, and then the resultant data concluded by the fuzzy decision-making system are used to launch respective robotic motions. The RGB camera and the infrared light module aim to do distance estimation of a body or several bodies. Findings – The modules not only provide image perception but also objective skeleton detection. In which, a laser source in the infrared light module emits invisible infrared light which passes through a filter and is scattered into a semi-random but constant pattern of small dots which is projected onto the environment in front of the sensor. The reflected pattern is then detected by an infrared camera and analyzed for depth estimation. Since the depth of object is a key parameter for pose recognition, one can estimate the distance to each dot and then get depth information by calculation of distance between emitter and receiver. Research limitations/implications – Future work will consider to reduce the computation time for objective estimation and to tune parameters adaptively. Practical implications – The experimental results demonstrate the feasibility of the proposed system. Originality/value – This paper achieves real-time human-robot interaction by visual detection based on structural light analysis.

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Du, Bing-Yi, Wu-Yang Yang, Jing Zhang, Xue-Shan Yong, Jian-Hu Gao, and Hai-Shan Li. "Matrix-fluid decoupling-based joint PP-PS-wave seismic inversion for fluid identification." GEOPHYSICS 84, no. 3 (May 1, 2019): R477—R487. http://dx.doi.org/10.1190/geo2017-0376.1.

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Seismic fluid identification is the main goal of current prestack seismic inversion. Various kinds of fluid indicators are used for fluid detection in industry today. However, the existing methods cannot always provide reliable fluid prediction owing to the insensitivity to fluid response and the lack of converted wave constraints. The equivalent fluid bulk modulus is an effective fluid factor based on matrix-fluid decoupling, which can provide persuasive evidence for fluid detection. Combining poroelasticity theory and matrix-fluid decoupling theory, we have deduced a new PS-wave linear amplitude versus offset approximation equation that provides estimations of equivalent fluid bulk modulus, rigidity, porosity, and density. Then, the joint inversion of PP- and PS-waves based on matrix-fluid decoupling was executed in a Bayesian framework with constraints from rock physics and well-log data obtaining elastic parameter estimation of high precision directly. We tested the new method on a synthetic example and field multicomponent data, and the results indicated that the estimated fluid factor matched with well-data interpretation and geology information because of adding converted wave information and avoiding indirect inversion error. This demonstrated that the new method can enhance the quality of fluid detection and provide reliable geophysical evidence for reservoir characterization.

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Journal articles on the topic 'Detection and estimation theory'

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