Abstract:

Distributed networked radar system (NRS) can take full advantage of multi-radar synergistic features to improve the tracking performance of a moving target. However, in fact, the limited total transmitted power and the high nonlinearity of the measurement function of an NRS heavily restrict the target tracking performance. To solve these problems, a high accuracy cooperative tracking and resource allocation method is proposed, in which the uncorrelated conversion based filter (UCF) is utilized to improve the estimation performance by extracting effective measurement information. Moreover, this estimation can be considered as the feedback information for the framework to further optimize the performance of the resource allocation. Firstly, the posterior Cramer-Rao lower bound (PCRLB) is derived as the optimization criterion, which can be utilized to obtain the optimized resource allocation. And then according to the allocated power, the target state can be estimated. A UCF is proposed for strongly nonlinear measurement functions, which utilizes uncorrelated conversion to extract more information from the original measurements and applies it to a linear minimum mean square error framework for state estimation, thereby improving the performance of target state estimation. The simulation results verify the effectiveness of the proposed method.

Key words: networked radar, power allocation, cooperative tracking, uncorrelated conversion, nonlinear filtering