Track-to-track association and ambiguity management in the presence of sensor bias

The track-to-track association problem is to determine the pairing of sensor-level tracks that correspond to the same true target from which the sensorlevel tracks originated. This problem is crucial for multisensor data fusion and is complicated by the presence of individual sensor biases, random errors, false tracks, and missed tracks. A popular approach to performing track-to-track association between two sensor systems is to jointly optimize the a posteriori relative bias estimate between the sensors and the likelihood of track-to-track association. Algorithms that solve this problem typically generate the K best bias-association hypotheses and corresponding bias-association likelihoods. In this paper, we extend the above approach in two ways. First, we derive a closed-form expression for computing “pure” track-to-track association likelihoods, as opposed to bias-association likelihoods which are weighted by a unique relative bias estimate. Second, we present an alternative formulation of the track-to-track association problem in which we optimize solely with respect to association likelihoods. These results facilitate what is commonly known as system-level track ambiguity management.

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