基于自适应卡尔曼滤波的机动目标跟踪算法

Tracking Algorithm for Maneuvering Target Based on Adaptive Kalman Filter

针对一般卡尔曼滤波融合跟踪方法无法实现对机动目标的有效跟踪问题,提出一种自适应卡尔曼滤波融合方法,设计一种能够提供目标开始机动瞬时估计的目标机动探测器,反复对目标的加速机动进行估计,当确定目标开始机动时,卡尔曼滤波模型将自适应地调整为目标机动状态模型。最后,通过仿真实验对比分析,证明文中所提方法优于一般卡尔曼滤波融合方法。

The application of kalman filter in tracking the maneuver target is not available as it is used in track- ing the target of uniform motion. Therefore, an improved method for tracking a maneuver target is proposed. By the proposed method, the maneuver detector provides the estimate of time instant at which a target starts to maneuver; and when a target maneuver is determined, the kalman filter model will be adjusted with varied target motion state. The maneuver, modeled as acceleration, is estimated recursively. Finally, the performance of the proposed ap- proach proves to be superior to that of the kalman filters by simulation.