王黎明,何茜,李会勇. 外辐射源MIMO雷达机会信源选择与接收站布置联合设计[J]. 电波科学学报,2023,38(2):253-260. DOI: 10.12265/j.cjors.2022080
      引用本文: 王黎明,何茜,李会勇. 外辐射源MIMO雷达机会信源选择与接收站布置联合设计[J]. 电波科学学报,2023,38(2):253-260. DOI: 10.12265/j.cjors.2022080
      WANG L M, HE Q, LI H Y. Joint design for illuminator of opportunity selection and receiver deployment in the passive MIMO radar system[J]. Chinese journal of radio science,2023,38(2):253-260. (in Chinese). DOI: 10.12265/j.cjors.2022080
      Citation: WANG L M, HE Q, LI H Y. Joint design for illuminator of opportunity selection and receiver deployment in the passive MIMO radar system[J]. Chinese journal of radio science,2023,38(2):253-260. (in Chinese). DOI: 10.12265/j.cjors.2022080

      外辐射源MIMO雷达机会信源选择与接收站布置联合设计

      Joint design for illuminator of opportunity selection and receiver deployment in the passive MIMO radar system

      • 摘要: 本文研究外辐射源多输入多输出(multiple-input multiple-output,MIMO)雷达目标位置和速度估计,通过控制接收站选用的机会信源个数来解决系统复杂度受限的问题. 将机会信源选择变量引入到外辐射源MIMO雷达信号模型,基于此推导最优参数估计器及相应的克拉美罗界(Cramer-Rao bound, CRB). 以CRB为系统参数估计性能评价标准,对机会信源优化选择、接收站优化布置、机会信源选择与接收站布置联合优化三种优化方式性能进行对比,三者分别属于整数型、连续型及混合整数型的优化问题. 仿真表明,在系统复杂度受限的情况下,三种优化方法都有利于提高外辐射源MIMO雷达的估计性能,但联合优化的性能最佳.

         

        Abstract: This paper studies the estimation of the target position and velocity in the passive multiple-input multiple-output (MIMO) radar, we control the number of illuminator of opportunity selected at the receiver to satisfy the limited system complexity requirement. The selection variables for the illuminator of opportunity are introduced into the signal model, based on which, the optimum receiver and the Cramer-Rao bound are derived. With the bound as a metric, we consider the illuminator of opportunity selection, the receiver deployment and the joint illuminator of opportunity selection and receiver deployment optimization, which are shown to be the integer, continuous and mix integer optimization problems respectively. Numerical examples shows that, under the case of limited complexity, all optimization approaches improve the system estimation performance, while the joint optimization tends to be the best.

         

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