一种基于目标运动模型的可变固定延迟的平滑算法

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一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。

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

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.