有色量测噪声下的HCKF及其应用

发布时间：2018-05-01 05:16

本文选题：非线性滤波 + 有色噪声　；参考：《计算机工程与应用》2017年14期

【摘要】：针对高阶容积卡尔曼滤波(HCKF)算法在有色量测噪声条件下滤波精度下降的问题,提出了有色量测噪声下的HCKF算法。通过一阶马尔科夫模型将有色量测噪声进行白化,将带有色量测噪声的非线性离散随机系统转化为白噪声下的非线性时滞系统,并给出高斯域内针对非线性时滞系统的贝叶斯滤波框架。利用高阶容积准则对该滤波框架进行近似计算,进而得到有色量测噪声下的HCKF算法。将所提算法应用到机动目标跟踪系统中,仿真实验结果表明,量测噪声为白噪声时,所提算法与标准HCKF算法具有相同的估计性能;在量测噪声为有色噪声时,所提算法相比于标准HCKF具有更优的估计精度和鲁棒性。
[Abstract]:Aiming at the problem that the filtering accuracy of high-order volumetric Kalman filter (HCKF) algorithm decreases under the condition of colored measurement noise, the HCKF algorithm for colored measurement noise is proposed. The colored measurement noise is whitened by the first-order Markov model, and the nonlinear discrete stochastic system with colored measurement noise is transformed into a nonlinear time-delay system with white noise. The Bayesian filtering framework for nonlinear time-delay systems in Gao Si domain is also given. The high order volumetric criterion is used to approximate the filter frame, and then the HCKF algorithm under colored measurement noise is obtained. The proposed algorithm is applied to the maneuvering target tracking system. The simulation results show that the proposed algorithm has the same estimation performance as the standard HCKF algorithm when the measurement noise is white noise, and when the measurement noise is colored noise, the proposed algorithm has the same estimation performance as the standard HCKF algorithm. Compared with the standard HCKF, the proposed algorithm has better estimation accuracy and robustness.
【作者单位】：空军工程大学航空航天工程学院;
【基金】：国家自然科学基金(No.61304120,No.61473307,No.61603411) 民口973项目(No.2015CB755802) 航空科学基金(No.20155896026)
【分类号】：TN713

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