Benefit Analysis of Data Fusion for Target Tracking in Multiple Radar System

With the recent development in radar technology, a multiple radar system (MRS) has become an attractive platform for target tracking. Technically speaking, data fusion among multiple radars can definitely enhance the tracking performance. However, the enhancement may not always be significant, as the improvement depends on several factors, such as the signal-to-noise ratio, the deployment, and the resolution of each radar. In this paper, a benefit analysis of data fusion for target tracking in MRS is developed. In particular, the analysis is on whether, for a given target in a given environment, the fusion between two radars is worthy to be implemented. First, the performance enhancement achieved by individual radar, in terms of the Bayesian Cramér-Rao lower bound, is derived as a recursive procedure. On this basis, a scalar parameter is then defined, according to which the decision on whether to fuse the data from two radars or use individual radar instead to track a target can be made. Finally, simulation results demonstrate the correctness of fusion rule defined in this paper.