The probabilistic data association filter (PDAF) is a suboptimal approach to tracking a target in the presence of clutter. In the PDAF implementation, the Kalman measurement update is performed over the set of validated measurements and the Kalman time update is used to propagate the PDAF measurement update. A popular approach to obtaining a numerically stable set of Kalman update equations is to propagate the U-D factors of the covariance in the measurement and time updates. The PDAF measurement update equation is obtained in U-D factored form by applying the modified weighted Gram-Schmidt (MWG-S) algorithm to the three factored terms. The factors of the first two terms are determined from the U-D factors of the a priori and conditional a posteriori covariances. The third term is factored analytically using the Agee-Turner factorization. The resulting U-D square-root PDAF is then applied to the problem of active tracking of a submarine in reverberation using polar coordinates.<>
Active sonar application of a U-D square root PDAF
IEEE Transactions on Aerospace and Electronic Systems ; 26 , 5 ; 850-857
1990-09-01
466850 byte
Article (Journal)
Electronic Resource
English
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