A New Approach to Multitarget Tracking Using Probabilistic Data Association
ELECTRONICS RESEARCH LAB ADELAIDE (AUSTRALIA)
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This report develops the theory for a multitarget tracking algorithm based on Probabilistic Data Association with new selection rules for assigning sensor measurements to target track and for forming multitrack clusters. These new rules remove the requirement to form a gate about each targets predicted position for the selection of sensor measurements. The resultant algorithm is the same for all target tracks and clutter conditions. The algorithm adapts to the sensor measurements via probability terms which model the environment and sensor processing. Keywords Automatic tracking Kalman filtering Probability theory Probabilistic data association Estimation Australia.
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