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For linear-Gaussian non-deterministic dynamics, that is, systems with non-zero process noise, it is well known that tracklet fusion based on equivalent measurement is optimal only for full communication rate, i.e., if the local posterior probabilities or estimates are communicated and fused after each observation and update time. Despite this constraint, tracklet fusion has become very popular because...
Track association has not received as much attention as track fusion in distributed multi-sensor multitarget tracking, especially for targets whose motion models involve process noise. One exception is an association metric that uses the cross-covariance of the track state estimates at a single time. For track fusion, it has been shown that the centralized state estimate can be obtained by fusion...
In track fusion, the measurements of individual sensors for each target are processed to generate local state estimates, which are then fused to obtain the global state estimate for the target. When there is no process noise or the fusion rate equals the sensor observation rate, the standard tracklet fusion or equivalent measurement fusion algorithm computes the optimal centralized estimate by extracting...
This paper formulates a simple two-sensor track association and fusion problem and derives a solution, using the finite point process formalism, expressed by prior and posterior Janossy measure density functions. The main objective of this paper is to show how the track association and fusion solution can be derived exclusively through manipulations of prior and posterior Janossy measure density functions...
Track fusion over a network of sensors requires association of the tracks before the state estimates can be combined. Track association generally involves two steps: evaluating an association metric to score each track-to-track association hypothesis, and selecting the best assignment between two sets of tracks. In many applications feature-aided track association can provide better performance than...
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