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A polynomial-time approximation algorithm for joint probabilistic data association

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2 Author(s)
Songhwai Oh ; Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA ; Sastry, S.

Joint probabilistic data association (JPDA) is a powerful tool for solving data association problems. However, the exact computation of association probabilities {βjk} in JPDA is NP-hard, where βjk is the probability that j-th observation is from k-th track. Hence, we cannot expect to compute association probabilities in JPDA exactly in polynomial time unless P = NP. In this paper, we present a simple Markov chain Monte Carlo data association (MCMCDA) algorithm that finds an approximate solution to JPDA in polynomial time. For ε > 0 and 0 < η < .5, we prove that the algorithm finds good estimates of βjk with probability at least 1 - η in time complexity O(ε-2 log η-1N(N log N + log ε-1))), where N is the number of observations.

Published in:

American Control Conference, 2005. Proceedings of the 2005

Date of Conference:

8-10 June 2005