This book presents the first truly accessible treatment of radar tracking; Kalman, Swerling, and Bayes filters for linear and nonlinear ballistic and satellite tracking systems; and the voltage–processing methods (Givens, Householder, and Gram–Schmidt) for least–squares filtering to correct for computer round–off errors. Tracking and Kalman Filtering Made Easy emphasizes the physical and geometric aspects of radar filters as well as the beauty and simplicity of their mathematics. An abundance of design equations, procedures, and curves allows readers to design tracking filters quickly and test their performance using only a pocket calculator!
The text incorporates problems and solutions, figures and photographs, and astonishingly simple derivations for various filters. It tackles problems involving clutter returns, redundant target detections, inconsistent data, track–start and track–drop rules, data association, matched filtering, tracking with chirp waveform, and more. The book also covers useful techniques such as the moving target detector (MTD) clutter rejection technique. All explanations are given in clear and simple terms, including:
- The voltage–processing approach to least–squares filtering
- The correlation between such procedures as discrete orthogonal Legendre polynomial (DOLP) and voltage processing
- The mathematical sameness of tracking and estimation problems on the one hand, and sidelobe canceling and adaptive array processing on the other
- The massively parallel systolic array sidelobe canceler processor
- Important computational accuracy issues
- An appended comparison between the Kalman and the Swerling filters, written by Dr. Peter Swerling
Tracking and Kalman Filtering Made Easy is invaluable for engineers, scientists, and mathematicians involved in tracking filter design. Its straightforward approach makes it an excellent textbook for senior–undergraduate and first–year graduate courses.
g and g–h–k Filters.
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.
Symbols and Acronyms.
Solution to Selected Problems.