Target Motion Analysis with A Priori Information.
ANALYSIS AND TECHNOLOGY INC NORTH STONINGTON CT
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The investigation of the value of incorporating a priori information into the TMA solution is continued in this study by computer simulations using an asymptotically optimum estimation procedure. Two modifications to the Maximum Likelihood and Maximum a Posteriori estimation methods for bearings only TMA are examined with a view toward their capability to improve numerical convergence of the solution during early parts of the problem. The modifications are made to develop procedures which optimally use all available measurements and information and to determine the true value of a priori information incorporated into an optimum procedure. The Gauss-Newton algorithm employed is modified such that at each solution iteration an optimum step is taken along the calculated direction. This is accomplished by a line-search minimization of the cost function logarithm of the a posteriori density function. The previous procedure iterated towards a null in the linearized gradient of the density function using only step-size bounds. The other modification examined is the estimation of an observable three-state relative-motion solution during the first leg which is then combined with a priori information to obtain the full solution after a maneuver. In addition to the study of the above modifications, multisensor MLE and MAP procedures are developed which solve the TMA problem using bearing and frequency measurements without own-ship maneuvers. Simualtions are run to ascertain the impact of range, speed, and center frequency a priori information on the numerical convergence and solution accuracy of the multisensor algorithm.
- Statistics and Probability