一种基于目标运动模型的可变固定延迟的平滑算法

Smoothing Approach with Diverse Fixed-lags Based on Target Motion Model

近来,对于机动目标跟踪的问题已经提出很多平滑方法。其中相互作用多模型-概率数据关联固定延迟算法(IMMPDAS)对在杂波环境下跟踪机动目标提供了一个较为有效的解决方法。然而,在此标准的平滑算法中,对于每一种模型采用相同的延迟间隔。提出了一种新的基于IMMPDA状态扩展系统的算法。它的改进性在于针对每种模型的复杂性采取不同的平滑延迟步幅,从而计算量将会大大降低,并且使用将更加灵活。通过对一个高度机动目标的多传感器跟踪的仿真实例来进行验证。仿真结果表明提出的平滑算法精度上与原有的平滑算法相差无几,都比已有的IMMPDA算法在航迹估计精度上有了显著提高,但却有更小的计算量。

Recently, lots of smoothing technique is presented for maneuvering target tracking. Interacting Multiple Model- Probabilistic Data Association （IMM-PDA） fixed-lag smoothing algorithm provides an efficient solution to track maneuvering target in cluttered environment. Whereas, the smoothing lag about each model in model set is set equally in traditional algorithm. A new approach was developed based on IMM-PDA approach to a state augmented system, but it adopts different smoothing lag according to the diverse complexity of each model. As a result, the application is more flexible and the computational load can be reduced greatly. Some simulations were made to track a highly maneuvering target in cluttered environment using two sensors. The results show that the proposed algorithm achieves a more significant improvement than comparative schemes in the accuracy of track estimation and with a lower computational load.