多级弹道目标主动段机动检测自适应跟踪算法

Adaptive tracking algorithm based on maneuver detection for multi-stage ballistic target boost phase

目前用于多级弹道目标主动段跟踪的"当前"统计模型无迹卡尔曼滤波算法在级间分离等强机动段会出现滤波误差大幅突跳的问题.通过理论分析和仿真指出滤波器参数不能随目标机动强弱自适应调整是根本原因.提出了一种基于滤波残差均值延迟相关的机动检测统计量,给出了其概率分布.仿真结果表明它比传统检测方法有效提高了检测性能.在此基础上给出了一种实时调整"当前"统计模型中机动频率的自适应跟踪算法.仿真结果表明,新算法能有效抑制误差突跳,加快滤波收敛速度,将主动段滤波精度提高一倍以上.

The filtering error will jump largely in strong maneuvering periods for multi-stage ballistic tar get boost phase tracking using unscented Kalman filter（UKF） based on current statistic（CS） model. Theory and simulation analyses pointed out that the key reason was the filter parameters can＇t be adjusted with the tar get maneuver strength. A new maneuver detection test statistic based on delay correlation of filtering residuals＇ mean was proposed, and its probability distribution was analyzed. Simulation results show that it efficiently im proves the maneuver detection performance than traditional methods. Then an adaptive tracking algorithm ad justing the CS model maneuvering frequency in real time was established. The simulation results illustrate that the new algorithm can effectively restrain the error jump, speed up the filtering convergence and improve the tracking accuracy by more than 100 percent.