基于模糊自适应变维Kalman滤波器的跟踪算法(英文)

针对不同阶数的Kalman滤波器具有不同的跟踪能力与跟踪效率之间存在的矛盾,设计了一种模糊自适应变维跟踪算法(FAVD)。该算法使用两级滤波器,根据目标机动性的变化,适当地调整滤波器的阶数,使跟踪结果快速收敛,很好地解决了矛盾。同时通过模糊推理机制,在线调节高阶滤波器的参数,使适用范围大大增强,提高自适应能力,从而使该算法可以采用较少的模型覆盖较多的目标运动模式,达到很好的跟踪滤波效果,计算量也会大大减小。通过对计算机仿真结果分析表明,提出的算法具有可靠、计算简便、快速等特点,模型滤波精度较高,并可实现实时跟踪预测,具有一定的理论价值和实用价值。

A Fuzzy and Adaptive Target Tracking Algorithm based on Two-stage Variable Dimension of Kalman Filter

The paper presents a fuzzy and adaptive target tracking algorithm designed to solve the contradiction between different tracking capacity and time of filters with different orders.The algorithm utilizes a two-stage Kalman filter method,and can adaptively adjust the order of the filter according to the variety of locomotive target so that the tracking results converges quickly on the premise of assuring the tracking accuracy.At the same time,some parameters in the fuzzy set are adjusted on-line based on fuzzy logic so that the adaptive ability to various target maneuvering patterns of this algorithm can be greatly improved-Better tracking performance can be achieved by using fewer models and computational burden can be reduced.The results of the instance simulation indicate that the algorithm proposed in this paper is reliable,simple and rapid,and the model has high estimation filtering,which can realize real-time trace and prediction and has definite value of both theory and practice.