Multi-sensor DP-TBD based on approximation of likelihood functions
Jinghe Wang, Wei Yi, Lingjiang Kong · 2017
In this paper, we address the target detection problem using multi-sensor dynamic programming based track before detect (DP-TBD) methods. First, we give two implementation methods of multi-sensor DP-TBD under the centralized processing and the distributed processing, respectively. Then, in order to improve the implementation efficiency of the multi-sensor DP-TBD, we further propose an improved DP-TBD method based on the approximation of local likelihood. Particularly, the proposed method first calculates the likelihood locally in the sensor nodes, then approximates the likelihood with a weighted sum of a number of basis functions, and finally transmits the weighted coefficients rather than all likelihood to the fusion center for further processing with DP-TBD. By this means, the proposed method can reduce the communication requirements of the system. In addition, since the likelihood are calculated locally, the computational burden of the fusion center can also be alleviated. The analysis and simulation results demonstrate that the proposed method can improve the implementation efficiency significantly with limited performance loss in comparison with the centralized/distributed processing DP-TBD.