Tracking in a cluttered environment with probabilistic data association

Authors:
Yaakov Bar-Shalom;Edison Tse
Affiliations:
Systems Control, Inc., Palo Alto, California 94304, U.S.A.;Systems Control, Inc., Palo Alto, California 94304, U.S.A.
Venue:
Automatica (Journal of IFAC)
Year:
1975

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Abstract

This paper presents a new approach to the problem of tracking when the source of the measurement data is uncertain. It is assumed that one object of interest ('target') is in track and a number of undesired returns are detected and resolved at a certain time in the neighbourhood of the predicted location of the target's return. A suboptimal estimation procedure that takes into account all the measurements that might have originated from the object in track but does not have growing memory and computational requirements is presented. The probability of each return (lying in a certain neighborhood of the predicted return, called 'validation region') being correct is obtained-this is called 'probabilistic data association' (PDA). The undesired returns are assumed uniformly and independently distributed. The estimation is done by using the PDA method with an appropriately modified tracking filter, called PDAF. Since the computational requirements of the PDAF are only slightly higher than those of the standard filter, the method can be useful for real-time systems. Simulation results obtained for tracking an object in a cluttered environment show the PDAF to give significantly better results than the standard filter currently in use for this type of problem.