Tracking and Kalman Filtering Made Easy
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English
A unique, easy-to-use guide to radar tracking and Kalmanfiltering
This book presents the first truly accessible treatment of radartracking; Kalman, Swerling, and Bayes filters for linear andnonlinear ballistic and satellite tracking systems; and thevoltage-processing methods (Givens, Householder, and Gram-Schmidt)for least-squares filtering to correct for computer round-offerrors. Tracking and Kalman Filtering Made Easy emphasizes thephysical and geometric aspects of radar filters as well as thebeauty and simplicity of their mathematics. An abundance of designequations, procedures, and curves allows readers to design trackingfilters quickly and test their performance using only a pocketcalculator!
The text incorporates problems and solutions, figures andphotographs, and astonishingly simple derivations for variousfilters. It tackles problems involving clutter returns, redundanttarget detections, inconsistent data, track-start and track-droprules, data association, matched filtering, tracking with chirpwaveform, and more. The book also covers useful techniques such asthe moving target detector (MTD) clutter rejection technique. Allexplanations are given in clear and simple terms, including:
* The voltage-processing approach to least-squares filtering
* The correlation between such procedures as discrete orthogonalLegendre polynomial (DOLP) and voltage processing
* The mathematical sameness of tracking and estimation problems onthe one hand, and sidelobe canceling and adaptive array processingon the other
* The massively parallel systolic array sidelobe canceler processor
* Important computational accuracy issues
* An appended comparison between the Kalman and the Swerlingfilters, written by Dr. Peter Swerling
Tracking and Kalman Filtering Made Easy is invaluable forengineers, scientists, and mathematicians involved in trackingfilter design. Its straightforward approach makes it an excellenttextbook for senior-undergraduate and first-year graduate courses.
This book presents the first truly accessible treatment of radartracking; Kalman, Swerling, and Bayes filters for linear andnonlinear ballistic and satellite tracking systems; and thevoltage-processing methods (Givens, Householder, and Gram-Schmidt)for least-squares filtering to correct for computer round-offerrors. Tracking and Kalman Filtering Made Easy emphasizes thephysical and geometric aspects of radar filters as well as thebeauty and simplicity of their mathematics. An abundance of designequations, procedures, and curves allows readers to design trackingfilters quickly and test their performance using only a pocketcalculator!
The text incorporates problems and solutions, figures andphotographs, and astonishingly simple derivations for variousfilters. It tackles problems involving clutter returns, redundanttarget detections, inconsistent data, track-start and track-droprules, data association, matched filtering, tracking with chirpwaveform, and more. The book also covers useful techniques such asthe moving target detector (MTD) clutter rejection technique. Allexplanations are given in clear and simple terms, including:
* The voltage-processing approach to least-squares filtering
* The correlation between such procedures as discrete orthogonalLegendre polynomial (DOLP) and voltage processing
* The mathematical sameness of tracking and estimation problems onthe one hand, and sidelobe canceling and adaptive array processingon the other
* The massively parallel systolic array sidelobe canceler processor
* Important computational accuracy issues
* An appended comparison between the Kalman and the Swerlingfilters, written by Dr. Peter Swerling
Tracking and Kalman Filtering Made Easy is invaluable forengineers, scientists, and mathematicians involved in trackingfilter design. Its straightforward approach makes it an excellenttextbook for senior-undergraduate and first-year graduate courses.
English
ELI BROOKNER, DSc, is a Consulting Scientist with Raytheon Company in Sudbury, Massachusetts. Over a long and distinguished career, he has conceived and/or designed numerous radar systems, including Wake Measurement Radar and the first TWT radar put into space. A world-renowned teacher and lecturer, he is editor and principal contributor of three previous books: Radar Technology, Aspects of Modern Radar, and Practical Phased Array Antenna Systems.
English
TRACKING, PREDICTION, AND SMOOTHING BASICS.
g and g-h-k Filters.
Kalman Filter.
Practical Issues for Radar Tracking.
LEAST-SQUARES FILTERING, VOLTAGE PROCESSING, ADAPTIVE ARRAYPROCESSING, AND EXTENDED KALMAN FILTER.
Least-Squares and Minimum-Variance Estimates for LinearTime-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 andKalman 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 KalmanFilter).
Bayes Algorithm with Iterative Differential Correction forNonlinear Systems.
Kalman Filter Revisited.
Appendix.
Problems.
Symbols and Acronyms.
Solution to Selected Problems.
References.
Index.
g and g-h-k Filters.
Kalman Filter.
Practical Issues for Radar Tracking.
LEAST-SQUARES FILTERING, VOLTAGE PROCESSING, ADAPTIVE ARRAYPROCESSING, AND EXTENDED KALMAN FILTER.
Least-Squares and Minimum-Variance Estimates for LinearTime-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 andKalman 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 KalmanFilter).
Bayes Algorithm with Iterative Differential Correction forNonlinear Systems.
Kalman Filter Revisited.
Appendix.
Problems.
Symbols and Acronyms.
Solution to Selected Problems.
References.
Index.
