Download or read book Suboptimal Target Tracking in Clutter Using a Generalized Probabilistic Data Association Algorithm written by Wai Ying Kan and published by . This book was released on 1996 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Simple tracking algorithms based upon nearest neighbor filtering do not correctly consider measurement origin uncertainty and, therefore, fail to perform well in situations of high target density and clutter. The optimal tracking algorithm for commonly used target-clutter models computes the posterior density of the target state conditioned on the past history of observations. This posterior density is a Gaussian mixture with the number of terms equal to the number of possible ways to associate observations and targets. Though a recursive algorithm may be developed for the optimal estimator, it requires exponentially growing memory and computation and is, therefore, unimplementable. In this paper a new suboptimal algorithm is proposed where approximation is done by naturally partitioning and grouping the target state estimates into a set of approximate sufficient statistics. A new criterion function is introduced in this approximation process. The well-known Probabilistic Data Association filter (PDAF) turns out to be a special case of the new algorithm. Comparisons are made for the proposed estimator versus the PDAF."