针对目标跟踪的分布式MIMO雷达资源分配算法

基于分布式多输入多输出雷达,针对目标跟踪精度的优化问题提出了一种联合资源优化分配算法。首先,推导了机动目标跟踪误差的贝叶斯克拉美罗下界(Bayesian Cramer Rao lower bound, BCRLB),由BCRLB可知其跟踪精度主要由信号发射功率、带宽和信号有效时宽决定。然后,以最小化目标的BCRLB为目标函数,建立了包含相应的3个资源变量的优化模型,分析可知该模型的求解是一个非凸问题的求解。所以采用循环最小化算法和凸松弛的方法将这个非凸的优化模型转化为凸优化模型进行求解。最后,仿真结果表明,利用所提出的资源分配算法能明显提高机动目标的跟踪精度。

Distributed MIMO radar resource allocation approach for target tracking

A joint resource allocation algorithm is proposed to improve the maneuvering target tracking accuracy of distributed multiple input multiple output radar. Firstly, the Bayesian Cramer Rao lower bound (BCRLB) on the mean square error in target location and velocity estimation is derived. It is not hard to know that the estimation accuracy is related with the transmitted power, signal bandwidth and signal effective time width by analyzing the BCRLB. Then, a criterion minimizing the BCRLB is derived, and the corresponding optimization model with these three resource variables is constructed. To solve this non convex problem, the cyclic minimization algorithm and the convex relaxation approach are adopted to transform it into a convex optimization problem. Finally, simulations demonstrate that the target tracking accuracy is improved by the proposed algorithm.