Готові списки джерел за темами / Detection and estimation theory

Добірка наукової літератури з теми "Detection and estimation theory"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Detection and estimation theory"

1

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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2

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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3

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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4

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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5

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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6

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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8

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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Дисертації з теми "Detection and estimation theory"

1

Feinstein, Jonathan S. "Detection controlled estimation : theory and applications." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14868.

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2

Wright, George Alfred Jr. "Nonparameter density estimation and its application in communication theory." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/14979.

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Warner, Carl Michael 1952. "ESTIMATION OF NONSTATIONARY SIGNALS IN NOISE (PROCESSING, ADAPTIVE, WIENER FILTERS, ESTIMATION, DIGITAL)." Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/291297.

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4

McElwain, Thomas P. "L-estimators used in CFAR detection." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/29199.

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Leong, Alex Seak Chon. "Performance of estimation and detection algorithms in wireless networks." Connect to thesis, 2007. http://repository.unimelb.edu.au/10187/2229.

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This thesis focuses on techniques for analyzing the performance of estimation and detection algorithms under conditions which could be encountered in wireless networks, with emphasis on wireless sensor networks. These include phenomena such as measurement losses, fading channels, measurement delays and power constraints.
We first look at the hidden Markov model (HMM) filter with random measurement losses. The loss process is governed by another Markov chain. In the two-state case we derive analytical expressions to compute the probability of error. In the multi-state case we derive approximations that are valid at high signal-to-noise ratio (SNR). Relationships between the error probability and parameters of the loss process are investigated.
We then consider the problem of detecting two-state Markov chains in noise, under the Neyman-Pearson formulation. Our measure of performance here is the error exponent, and we give methods for computing this, firstly when channels are time-invariant, and then for time-varying fading channels. We also characterize the behaviour of the error exponent at high SNR.
We will look at the fixed lag Kalman smoother with random measurement losses. We investigate both the notion of estimator stability via expectation of the error covariance, and a probabilistic constraint on the error covariance. A comparison with the Kalman filter where lost measurements are retransmitted is made.
Finally we consider the distributed estimation of scalar linear systems using multiple sensors under the analog forwarding scheme. We study the asymptotic behaviour of the steady state error covariance as the number of sensors increases. We formulate optimization problems to minimize the sum power subject to error covariance constraints, and to minimize the error covariance subject to sum power constraints. We compare between the performance of multi-access and orthogonal access schemes, and for fading channels the effects of various levels of channel state information (CSI).
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6

Yang, Zaiyue. "Fault detection, estimation and control of periodically excited nonlinear systems." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B40887984.

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Yang, Zaiyue, and 楊再躍. "Fault detection, estimation and control of periodically excited nonlinear systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40887984.

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Xu, Cuichun. "Statistical processing on radar, sonar, and optical signals /." View online ; access limited to URI, 2008. http://0-digitalcommons.uri.edu.helin.uri.edu/dissertations/AAI3328735.

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Lu, Jingyang. "Resilient dynamic state estimation in the presence of false information injection attacks." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4644.

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The impact of false information injection is investigated for linear dynamic systems with multiple sensors. First, it is assumed that the system is unaware of the existence of false information and the adversary is trying to maximize the negative effect of the false information on Kalman filter's estimation performance under a power constraint. The false information attack under different conditions is mathematically characterized. For the adversary, many closed-form results for the optimal attack strategies that maximize the Kalman filter's estimation error are theoretically derived. It is shown that by choosing the optimal correlation coefficients among the false information and allocating power optimally among sensors, the adversary could significantly increase the Kalman filter's estimation errors. In order to detect the false information injected by an adversary, we investigate the strategies for the Bayesian estimator to detect the false information and defend itself from such attacks. We assume that the adversary attacks the system with certain probability, and that he/she adopts the worst possible strategy that maximizes the mean squared error (MSE) if the attack is undetected. An optimal Bayesian detector is designed which minimizes the average system estimation error instead of minimizing the probability of detection error, as a conventional Bayesian detector typically does. The case that the adversary attacks the system continuously is also studied. In this case, sparse attack strategies in multi-sensor dynamic systems are investigated from the adversary's point of view. It is assumed that the defender can perfectly detect and remove the sensors once they are corrupted by false information injected by an adversary. The adversary's goal is to maximize the covariance matrix of the system state estimate by the end of attack period under the constraint that the adversary can only attack the system a few times over the sensor and over the time, which leads to an integer programming problem. In order to overcome the prohibitive complexity of the exhaustive search, polynomial-time algorithms, such as greedy search and dynamic programming, are proposed to find the suboptimal attack strategies. As for greedy search, it starts with an empty set， and one sensor is added at each iteration, whose elimination will lead to the maximum system estimation error. The process terminates when the cardinality of the active set reaches to the sparsity constraint. Greedy search based approaches such as sequential forward selection (SFS), sequential backward selection (SBS), and simplex improved sequential forward selection (SFS-SS) are discussed and corresponding attack strategies are provided. Dynamic programming is also used in obtaining the sub-optimal attack strategy. The validity of dynamic programming lies on a straightforward but important nature of dynamic state estimation systems: the credibility of the state estimate at current step is in accordance with that at previous step. The problem of false information attack on and the Kalman filter's defense of state estimation in dynamic multi-sensor systems is also investigated from a game theoretic perspective. The relationship between the Kalman filter and the adversary can be regarded as a two-person zero-sum game. The condition under which both sides of the game will reach a Nash equilibrium is investigated.
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10

Ling, Tao. "High resolution gamma detector for small-animal positron emission tomography /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/9751.

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Книги з теми "Detection and estimation theory"

1

1945-, Papantoni-Kazakos P., ed. Detection and estimation. New York: Computer Science Press, 1990.

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2

Detection, estimation, and modulation theory. New York: Wiley, 2001.

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3

Detection, estimation, and modulation theory. New York: Wiley-Interscience, 2002.

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4

Detection, estimation, and modulation theory. New York: Wiley, 2001.

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5

Trees, Harry L. Van. Detection, estimation, and modulation theory. Hoboken, N.J: Wiley, 2003.

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6

Detection, estimation, and modulation theory. Malabar, Fla: Krieger Pub. Co., 1992.

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7

Signal detection and estimation. 2nd ed. Boston: Artech House, 2005.

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8

Barkat, Mourad. Signal detection and estimation. Boston: Artech House, 1991.

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9

Poor, H. Vincent. An introduction to signal detection and estimation. New York: Springer-Verlag, 1988.

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10

An introduction to signal detection and estimation. 2nd ed. New York: Springer-Verlag, 1994.

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Частини книг з теми "Detection and estimation theory"

1

Levy, Bernard C. "Parameter Estimation Theory." In Principles of Signal Detection and Parameter Estimation, 1–56. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76544-0_4.

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2

Hautus, Michael J., Neil A. Macmillan, and C. Douglas Creelman. "Adaptive Methods for Estimating Empirical Thresholds." In Detection Theory, 253–79. 3rd ed. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003203636-14.

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3

D’ariano, G. M. "Quantum Estimation Theory and Optical Detection." In Quantum Optics and the Spectroscopy of Solids, 139–74. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-015-8796-9_8.

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4

Gopi, E. S. "Detection Theory and Estimation Theory for Wireless Communication." In Digital Signal Processing for Wireless Communication using Matlab, 51–101. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-20651-6_2.

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Gopi, E. S. "Detection Theory and Estimation Theory for Wireless Communication." In Digital Signal Processing for Wireless Communication using Matlab, 93–139. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82036-7_3.

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6

Li, Qihu. "Detection and Estimation Theory of Digital Signals." In Advanced Topics in Science and Technology in China, 81–149. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-18290-7_3.

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7

Giua, Alessandro. "State Estimation and Fault Detection Using Petri Nets." In Applications and Theory of Petri Nets, 38–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21834-7_3.

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8

Huang, Y. F. "Series Expansions in Statistical Theory of Detection and Estimation." In Communications and Networks, 47–71. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4904-7_4.

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Scott, D. W. "Partial Mixture Estimation and Outlier Detection in Data and Regression." In Theory and Applications of Recent Robust Methods, 297–306. Basel: Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7958-3_26.

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Madhu Chandra, G., and G. M. Sreeramareddy. "Simple Modeling of Mobile Foreground Detection Using Probabilistic Linear Estimation Approach." In Intelligent Systems in Cybernetics and Automation Control Theory, 58–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00184-1_6.

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Тези доповідей конференцій з теми "Detection and estimation theory"

1

Baygun, B., and A. O. Hero. "An order selection criterion via simultaneous estimation/detection theory." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205568.

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2

"Sessions: detection and estimation." In 1988 IEEE International Symposium on Information Theory. IEEE, 1988. http://dx.doi.org/10.1109/isit.1988.22235.

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3

Moustakides, George V. "Optimum joint detection and estimation." In 2011 IEEE International Symposium on Information Theory - ISIT. IEEE, 2011. http://dx.doi.org/10.1109/isit.2011.6034125.

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4

VANNUCCI, ARMANDO, and MARIA GABRIELLA DI BENEDETTO. "SIGNAL PROCESSING: ELEMENTS OF DETECTION AND ESTIMATION THEORY." In Proceedings of the International Summer School. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812792846_0015.

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Grossi, Emanuele, and Marco Lops. "Joint sequential detection and estimation of Markov targets." In 2008 IEEE Information Theory Workshop (ITW). IEEE, 2008. http://dx.doi.org/10.1109/itw.2008.4578675.

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Reid, Aaron B. "Blind constellation estimation and detection using cluster analysis." In 2011 Australian Communications Theory Workshop (AusCTW). IEEE, 2011. http://dx.doi.org/10.1109/ausctw.2011.5728758.

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Amleh, Khaled, and Hongbin Li. "Channel Estimation and Detection for Single Carrier Systems." In 2009 Australian Communications Theory Workshop (AusCTW). IEEE, 2009. http://dx.doi.org/10.1109/ausctw.2009.4805597.

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Suzuki, Joe. "Mutual Information Estimation: Independence Detection and Consistency." In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849612.

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Dentamaro, Vincenzo, Donato Impedovo, and Giuseppe Pirlo. "Fall Detection by Human Pose Estimation and Kinematic Theory." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9413331.

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"CORE POINT DETECTION USING FINE ORIENTATION FIELD ESTIMATION." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001089503510356.

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Звіти організацій з теми "Detection and estimation theory"

1

Duncan, Victoria Stephanie. Radiation Detection Theory. Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1505948.

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2

Bar-Shalom, Y., and K. R. Pattipati. Estimation with Multisensor/Multiscan Detection Fusion. Fort Belvoir, VA: Defense Technical Information Center, March 1992. http://dx.doi.org/10.21236/ada250496.

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Santosa, Fadil. Estimation With Multisensor/Multiscan Detection Fusion. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada265673.

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Chow, Winston C. Estimation Theory with Fractional Gaussian Noise. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada301443.

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Kailath, Thomas. Studies in Estimation Theory, Applications and Implementations. Fort Belvoir, VA: Defense Technical Information Center, June 1989. http://dx.doi.org/10.21236/ada210712.

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Kailath, Thomas. Studies in Estimation Theory, Applications and Implementation. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada256964.

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Helpman, Elhanan, Oleg Itskhoki, Marc-Andreas Muendler, and Stephen Redding. Trade and Inequality: From Theory to Estimation. Cambridge, MA: National Bureau of Economic Research, April 2012. http://dx.doi.org/10.3386/w17991.

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Varshney, Pramod K. Distributed Detection Theory and Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, December 1999. http://dx.doi.org/10.21236/ada374837.

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Varshney, Pramod K. Distributed Detection Theory and Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada280410.

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Varshney, Pramod K. Distributed Detection Theory and Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada301116.

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