空基无源相干定位系统的机动目标跟踪算法

针对空基无源相干定位系统中外辐射源状态不确定性对机动目标跟踪精度的影响, 提出了一种基于多模型预测的双变量容积卡尔曼滤波算法。首先建立了机动目标跟踪的系统模型, 并确定了多模型集。然后基于多模型思想, 将模型交互步骤增加到状态预测步骤之后, 对状态预测值进行交互融合, 得到最优的状态预测值。为解决固定的马尔可夫转移概率导致系统跟踪性能下降的问题, 采用“感知记忆”嵌入的时变转移概率, 降低不匹配模型的竞争影响; 最后利用双变量容积卡尔曼滤波算法同时对目标和外辐射源进行状态估计。仿真对比实验验证了算法的有效性。

Maneuvering target tracking algorithm for airborne passive coherent localization system

A bivariate cubature Kalman filter algorithm based on multiple model prediction is proposed to deal with the influence of the state uncertainty of airborne passive coherent localization system on the tracking accuracy of maneuvering targets.Firstly,the system model of maneuvering targets tracking is established and the multiple model set is determined.Then,based on the idea of multiple models,the model interaction step is added to the state prediction step,and the predicted state value is interactively fused to obtain the optimal state forecast value.In order to solve the problem that the fixed Markov transition probability leads to the decline of the tracking performance of the system,the time-varying transition probability embedded in the“perception memory”is adopted to reduce the competitive influence of the mismatched model.Finally,the bivariate cubature Kalman filter algorithm is used to estimate the state of both target and external transmitter.Simulation and comparison experiments verify the effectiveness of the algorithm.