组网雷达系统高精度协同跟踪和功率分配方法

High accuracy cooperative tracking and power allocation method in networked radar system

分布式组网雷达系统可充分利用多雷达协同优势提升动目标跟踪性能。然而,在实际中,组网雷达系统的发射总功率受限且量测函数的高度非线性,都会导致目标跟踪精度极大受限。针对上述问题,提出一种基于不相关转换滤波(uncorrelated conversion based filter,UCF)的高精度目标协同跟踪和资源管理方法,该滤波方法可充分提取有效量测信息,提升目标状态估计性能,且该信息可作为整体框架的反馈信息进一步优化资源分配。首先推导了后验克拉美罗下界作为优化准则,利用该准则给出当前时刻的最优资源分配;然后基于分配的功率资源,进行目标状态估计;针对强非线性量测函数,提出一种UCF,利用不相关转换提取更多原始量测中的信息,并将其用于线性最小均方误差框架进行状态估计,从而提高目标状态估计性能。仿真结果验证了所提方法的有效性。

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.