摘要
为解决标准求容积卡尔曼滤波器在有色量测噪声条件下滤波精度退化的问题,提出改进求容积卡尔曼滤波器及其平方根形式.首先利用一阶马尔科夫模型白化非线性离散随机系统中有色量测噪声,将有色量测噪声下非线性离散随机系统转化为白噪声下非线性时滞系统.然后根据所得非线性时滞系统推导其高斯域的贝叶斯滤波框架,最后基于3度Spherical-Radial规则将该滤波框架近似为改进的求容积卡尔曼滤波器和其平方根形式.机动目标跟踪仿真试验结果表明两种改进求容积卡尔曼滤波算法在标准白噪声条件下与标准求容积卡尔曼滤波算法的估计精度相同,而在有色量测噪声背景下滤波精度和鲁棒性更优.
To solve the estimation accuracy degradation of standard cubature Kalman filter with colored measurement noise,an improved cubature Kalman filter and its square root form are presented in the paper.Firstly,the first-order Markov model is used to whiten colored measurement noise in nonlinear discrete stochastic system,and then the nonlinear discrete stochastic system with colored measurement noise is transformed into a nonlinear time-delay system with normal white noise.Secondly,the frame of recursive Bayesian filter in Gaussian domain is derived based on the whitened nonlinear time-delay system.Finally,third-degree Spherical-Radial cubature rule is applied in above frame to deduce the improved Cubature Kalman filter and its square root form.The maneuvering target tracking simulation results demonstrate the improved cubature Kalman filters have the same accuracy as standard ones in the system with normal white Gaussian noise,and can achieve better accuracy and robustness when the measurement noise is colored.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2015年第1期1-10,共10页
Control Theory & Applications
基金
国家高技术研究发展计划("863"计划)项目(2011AA110201)资助~~
