A review of effective radar tracking filter methods and their associated digital filtering algorithms. It examines newly developed systems for eliminating the real-time execution of complete recursive Kalman filtering matrix equations that reduce tracking and update time. It also focuses on the role of tracking filters in operations of radar data processors for satellites, missiles, aircraft, ships, submarines and RPVs.

A review of effective radar tracking filter methods and their associated digital filtering algorithms. It examines newly developed systems for eliminating the real-time execution of complete recursive Kalman filtering matrix equations that reduce tracking and update time. It also focuses on the role of tracking filters in operations of radar data processors for satellites, missiles, aircraft, ships, submarines and RPVs.

TRACKING, PREDICTION, AND SMOOTHING BASICS. g and g-h-k Filters. Kalman Filter. Practical Issues for Radar Tracking. LEAST-SQUARES FILTERING, VOLTAGE PROCESSING, ADAPTIVE ARRAY PROCESSING, AND EXTENDED KALMAN FILTER. Least-Squares and Minimum-Variance Estimates for Linear Time-Invariant Systems. Fixed-Memory Polynomial Filter. Expanding- Memory (Growing-Memory) Polynomial Filters. Fading-Memory (Discounted Least-Squares) Filter. General Form for Linear Time-Invariant System. General Recursive Minimum-Variance Growing-Memory Filter (Bayes and Kalman Filters without Target Process Noise). Voltage Least-Squares Algorithms Revisited. Givens Orthonormal Transformation. Householder Orthonormal Transformation. Gram--Schmidt Orthonormal Transformation. More on Voltage-Processing Techniques. Linear Time-Variant System. Nonlinear Observation Scheme and Dynamic Model (Extended Kalman Filter). Bayes Algorithm with Iterative Differential Correction for Nonlinear Systems. Kalman Filter Revisited. Appendix. Problems. Symbols and Acronyms. Solution to Selected Problems. References. Index.

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.

Sensor data fusion is the process of combining error-prone, heterogeneous, incomplete, and ambiguous data to gather a higher level of situational awareness. In principle, all living creatures are fusing information from their complementary senses to coordinate their actions and to detect and localize danger. In sensor data fusion, this process is transferred to electronic systems, which rely on some "awareness" of what is happening in certain areas of interest. By means of probability theory and statistics, it is possible to model the relationship between the state space and the sensor data. The number of ingredients of the resulting Kalman filter is limited, but its applications are not.

The definitive textbook and professional reference on Kalman Filtering – fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.

Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation 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. Other features include: * Problems that apply theoretical material to real-world applications * In-depth coverage of the Interacting Multiple Model (IMM) estimator * Companion DynaEst(TM) software for MATLAB(TM) implementation of Kalman filters and IMM estimators * Design guidelines for tracking filters 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.