Paper
28 November 1983 An Optically Implemented Multiple-Stage Kalman Filter Algorithm
William A. Roemer, Peter S. Maybeck
Author Affiliations +
Abstract
A working paper is presented on the mathematical development and analysis of an optically implemented multiple-stage Kalman filter algorithm which uses two previously developed estimation models (linear first-order Gauss-Markov and constant turn-rate) for high energy laser pointing and tracking. An overview of the estimation models reveals model equivalence in mid- to long-range tracking applications and superiority of the constant turn-rate model (at the expense of a much higher computational burden) for both short-range and evasive target tracking. Real world constraints are to be forcibly imposed on the optical filter by limiting the choice of all system components to off-the-shelf units whose performance criteria are well characterized. Derivation of the filter architecture subject to the real world constraints shows the pielined iterative systolic array architecture to be significantly superior. Filter development based on this architecture is expected to generate a MTF which yields superior performance of the optical filter over its electronic counterpart based both on the output statistics produced and system throughput capability Additional analyses of filter performance reveal potential filter enhancement with the incorporation of range and relative velocity data obtained through use of a laser doppler velocimeter and an optical heterodyne detector. Current and planned future research efforts are also presented.
© (1983) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
William A. Roemer and Peter S. Maybeck "An Optically Implemented Multiple-Stage Kalman Filter Algorithm", Proc. SPIE 0431, Real-Time Signal Processing VI, (28 November 1983); https://doi.org/10.1117/12.936462
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CITATIONS
Cited by 7 scholarly publications.
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KEYWORDS
Algorithm development

Forward looking infrared

Data modeling

Filtering (signal processing)

Detection and tracking algorithms

Performance modeling

Stochastic processes

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