基于PDA—UKF的中段弹道目标红外多传感器融合跟踪算法

Infrared Multi-Sensor Fusion Midcourse Ballistic Object Tracking Algorithm Based on PDA-UKF

利用红外传感器跟踪中段弹道目标属于仅测角被动定位体制，需要融合多传感器信息实现目标高精度立体跟踪。同时，传感器视场内存在虚警干扰，为准确跟踪目标必须排除虚警。提出基于PDA．UKF（ProbabilityDataAssociation．UnscentedKalmanFilter）的弹道目标红外多传感器融合跟踪算法。该算法将PDA扩展到被动多传感器的融合跟踪应用，解决波门内出现虚警时引起的数据关联问题，结合UKF滤波方法解决中段弹道目标的非线性跟踪滤波问题。仿真结果表明，该算法能稳定、有效地对不同虚警条件下的中段弹道目标进行高精度实时跟踪，且计算量小，适合于空间预警应用。

Midcourse ballistic object tracking via infrared sensors belongs to bearing-only passive location and tracking structure, which needs multi-sensor fusion to realize high precise stereo tracking. Multi-sensor fusion tracking algorithm based on PDA-UKF（Probability Data Association-Unscented Kalman Filter）is presented. The algorithm extends PDA into multi-sensor fusion application, solving for the data association problem resulted from false alarm within predictive threshold area, and UKF is applied to solve for the nonlinear problem of midcourse target state evolution model and measurement model. Simulation results indicate that, in different false alarm conditions, the algorithm can effectively track midcourse ballistic object with high accuracy and run in real time.