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.

• 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.

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.

• 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.