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Estimation with Applications to Tracking and NavigationJanuary 2002
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Abstract
From the Publisher:
"Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics." "The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimation with a focus on applying these techniques to real systems." "Suitable for graduate engineering students and engineers working in remote sensors and tracking, Estimation with Applications to Tracking and Navigation provides expert coverage of this important area."--BOOK JACKET.
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- Xie Y, Huang Y and Song T (2018). Iterative joint integrated probabilistic data association filter for multiple-detection multiple-target tracking, Digital Signal Processing, 72:C, (232-243), Online publication date: 1-Jan-2018.
- Kwak K, Min J and Ahn S Incremental model learning and building of unknown objects 2017 17th International Conference on Control, Automation and Systems (ICCAS), (711-716)
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- Fang X, Nan L, Jiang Z and Chen L (2017). Noise-aware fingerprint localization algorithm for wireless sensor network based on adaptive fingerprint Kalman filter, Computer Networks: The International Journal of Computer and Telecommunications Networking, 124:C, (97-107), Online publication date: 4-Sep-2017.
- Huang K, Tan S, Luo Y, Guo X and Wang G (2017). Enhanced radio tomographic imaging with heterogeneous Bayesian compressive sensing, Pervasive and Mobile Computing, 40:C, (450-463), Online publication date: 1-Sep-2017.
- Liu X, Yu Y, Li Z, Iu H and Fernando T (2017). A novel constant gain Kalman filter design for nonlinear systems, Signal Processing, 135:C, (158-167), Online publication date: 1-Jun-2017.
- Afshari H, Gadsden S and Habibi S (2017). Gaussian filters for parameter and state estimation, Signal Processing, 135:C, (218-238), Online publication date: 1-Jun-2017.
- Chai J, Wu C, Zhao C, Chi H, Wang X, Ling B and Teo K (2017). Reference tag supported RFID tracking using robust support vector regression and Kalman filter, Advanced Engineering Informatics, 32:C, (1-10), Online publication date: 1-Apr-2017.
- Yang S and Baum M Extended Kalman filter for extended object tracking 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (4386-4390)
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- (2016). A Gaussian mixture Bernoulli filter for extended target tracking with application to an ultra-wideband localization system, Digital Signal Processing, 57:C, (1-12), Online publication date: 1-Oct-2016.
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- Djogatović M, Stanojević M and Mladenović N (2014). A variable neighborhood search particle filter for bearings-only target tracking, Computers and Operations Research, 52:PB, (192-202), Online publication date: 1-Dec-2014.
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- Prabhu U, Seshadri K and Savvides M Automatic facial landmark tracking in video sequences using kalman filter assisted active shape models Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I, (86-99)
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- Himmelsbach M, Luettel T and Wuensche H Real-time object classification in 3D point clouds using point feature histograms Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems, (994-1000)
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- Franke U, Rabe C, Badino H and Gehrig S 6D-vision Proceedings of the 27th DAGM conference on Pattern Recognition, (216-223)
- Meyer F and Win M Data Association for Tracking Extended Targets MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM), (337-342)
- Spangenberg R, Goehring D and Rojas R Pole-based localization for autonomous vehicles in urban scenarios 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2161-2166)
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