Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House Radar Library) (Artech House Radar Library (Hardcover))

For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian problems. To solve problems beyond this restricted class, particle filters are proving to be dependable methods for stochastic dynamic estimation. Packed with 867 equations, this cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defense surveillance systems, and examines defense-related applications of particle filters to nonlinear and non-Gaussian problems.

With this hands-on guide, you can develop more accurate and reliable nonlinear filter designs and more precisely predict the performance of these designs. You can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bi-static radar tracking, passive ranging (bearings-only tracking) of maneuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.

• Bayesian Multiple Target Tracking, Second Edition
• Dynamic Programming and Optimal Control (2 Vol Set)
• Machine Learning: An Algorithmic Perspective, Second Edition (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
• Kalman Filter for Beginners: with MATLAB Examples
• Fundamentals of Radar Signal Processing, Second Edition (McGraw-Hill Professional Engineering)
• Sequential Monte Carlo Methods in Practice (Statistics for Engineering and Information Science)
• Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control)
• Matrix Computations (Johns Hopkins Studies in the Mathematical Sciences)
• Monte Carlo Statistical Methods (Springer Texts in Statistics)
• Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)