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中文摘要：
有色噪声干扰情况下非线性系统的状态估计是许多实际工程需要解决的问题。该文章针对传统Kalman滤波器噪声统计特性未知时,受色噪声的影响精度严重降低,甚至出现发散等现象,设计了一种基于神经模糊网络的自适应的Kalman滤波跟踪器。该滤波器通过利用神经模糊网络作为误差估计器,估计出Kalman滤波器的估计误差,从而对Kalman滤波跟踪器的预测结果进行修正,得到更优的预测值。计算机仿真结果表明,该算法可以克服传统算法的局限性,有效地防止滤波器发散,缩小实际的滤波误差,提高滤波精度,实现对跟踪结果的在线改进。
英文摘要：
Estimation in nonlinear system with Colored Noises is problem in many projects.The traditional Kalman Filter is still deficient in tracking targets in the nonlinear systems with colored noises.An adaptive Kalman tracking algorithm based on neuro-fuzzy network is proposed in the paper.The estimation error is obtained online to modify the filtered result with neuro-fuzzy network as the estimator.The analysis of simulation results indicates preliminarily that our better tracking algorithm does restrain colored noise and improve that tracking accuracy.At same time it can reduce error of traditional algorithm and improve the tracking accuracy of the system online.
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