基于GA-ML-PMHT的多基站无源协同定位方法

针对无源协同定位系统中低可观测目标的航迹初始及维持问题,提出一种基于遗传算法的极大似然概率多假设的多基站无源协同定位方法。首先,建立多基站无源协同定位系统数学模型。其次,提出基于极大似然概率多假设的无源协同定位航迹初始算法,并首次利用遗传算法解决极大似然概率多假设中的优化求解问题,以提高目标检测跟踪性能。最后,通过滑窗法实现航迹维持。仿真结果表明,所提方法能够有效解决多基站无源协同定位系统中低可观测目标的航迹初始及维持问题。

A GA-ML-PMHT Based Multistatic Passive Coherent Location Method

In order to track very low observable targets with a multistatic passive coherent location system,a genetic algorithm maximum likelihood probabilistic multi-hypothesis method is proposed. The contributions consist of three aspects. First, the mathematical model for target detection and tracking is established. Second, a maximum likelihood probabilistic multi-hypothesis method is presented for track initialization,and the genetic algorithm is used for optimization and hence the estimation performance. Last, the track maintenance is achieved in a sliding window manner. Simulation results show the effectiveness of the proposed algorithm.