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Sequential track-to-track fusion algorithm: exact solution and approximate implementation

Track-to-track fusion (T2TF) is very important in distributed tracking systems. When tracks of a target at different sensors are fused for increased accuracy, an important issue is to account for the crosscorrelations among the tracks. In this paper, an exact solution for the general problem of T2TF is proposed. It can be used with various information structures, e.g., memoryless T2TF or sequential T2TF with information feedback at arbitrary times. Simulation results for a 1-D tracking scenario evaluate the benefit of the various configurations for T2TF. It is also observed that T2TF, although done optimally, can be suboptimal w.r.t. centralized measurement fusion. This is because the locally optimal filter gains are, in general, globally suboptimal. Furthermore, it is shown that feedback can lead to degradation of the accuracy of the (optimally) fused tracks. Based on the exact T2TF algorithms, an approximate implementation which requires less communications between the fusion center and the local trackers is also proposed. This allows the algorithms to be implemented in distributed tracking systems with low communication capacity. Examples of tracking in two dimensions with two radars, show that the proposed T2TF algorithms are consistent and can provide significant improvement in accuracy over unfused tracks. For the sensors-target geometry considered, the T2TF algorithm can even meet the performance bound of the centralized measurement fusion at the fusion times.