Improved Regularized Particle Filter Algorithm for SINS/SAR Integrated Navigation
This paper presents a new improved regularized particle filter algorithm for SINS/SAR (Strap-down Inertial Navigation System /Synthetic Aperture Radar) integrated navigation system. By adopting MCMC (Markov Chain Monte Carlo) move to the regularization process, a MCMC based filtering algorithm is developed through combining local resampling with MCMC move to prevent a large number of particles from being rejected. The proposed particle filtering method prevents the degeneracy of particles and guarantees that the resultant particles have a common distribution with the practical probability function, without causing extra noises on the estimates. It also reduces the estimation variance and the computational load. By using improved regularized particle filter algorithm and extended Kalman filter algorithm, simulate for the SINS/SAR integrated navigation system. Experimental results demonstrate that the improved regularized particle filter algorithm can reduce the navigation positioning errors, and filtering performance obviously exceeds the extended Kalman filter algorithm.
SINS/SAR integrated navigation improved regularized particle filter kalman filter markov chain monte carlo
Yi Gao Shesheng Gao Yu Gu
School of Automatics Northwestern Polytechnical University 710072, Xian, Shaanxi, P.R.China
国际会议
太原
英文
70-74
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）