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Exact finite dimensional nonlinear filters with certain diffusion nonlinear drift,” Stochastics, Vol. 5, 1981, pp. 65-92. [13] Daum, F. , “Exact finite dimensional nonlinear filters,” IEEE Trans. Automatic Control, Vol. 31, No. 7, 1986, pp. 616-622. [14] Daum, F. , “Beyond Kalman filters: practical design for nonlinear filters,” in Proceedings SPIE, Vol. 2561, 1995, pp. 252-262. [15] Sorenson, H. , “Recursive Estimation for nonlinear dynamic systems,” in Bayesian Analysis of Time Series and Dynamic Models, J.
This produces a constant range rate for the target. Let (r , r ) represent a range and range-rate cell in the target state space. The likelihood ratio in cell (r , r ) is displaced to the cell (r + Dt r, r ) over a time interval Dt. In this motion model, each velocity hypothesis can be treated independently. If the radar observations are consistent with a velocity hypothesis, peaks will develop in the likelihood ratio function at position-velocity cells consistent with that hypothesis. Viewed over time, the peaks will lie along a straight line corresponding to the target’s track in range versus time.
Exact finite dimensional nonlinear filters,” IEEE Trans. Automatic Control, Vol. 31, No. 7, 1986, pp. 616-622. [14] Daum, F. , “Beyond Kalman filters: practical design for nonlinear filters,” in Proceedings SPIE, Vol. 2561, 1995, pp. 252-262. [15] Sorenson, H. , “Recursive Estimation for nonlinear dynamic systems,” in Bayesian Analysis of Time Series and Dynamic Models, J. C. ), New York: Marcell Decker, 1988. [16] Sorenson, H. , “On the development of practical nonlinear filters,” Information Sciences, Vol.
