A Novel Adaptive Square Root Recursive Least Squares Filter Algorithm of SINS/SAR Integrated Navigation Systems
In strapdown inertial navigation system (SINS) synthetic aperture radar (SAR) integrated navigation systems, the time-varying parameter estimation is a challenging problem because SAR measurements are fairly few and have unequal interval. In such case, the common Kalman filter algorithm cant be convergent. Furthermore, the traditional recursive least squares (RLS) algorithm is difficult to estimate the time-varying parameter. This paper proposed a novel adaptive square root recursive least squares (ASRRLS) filter algorithm of SINS/SAR integrated navigation systems. The first characteristic of ASRRLS is introducing adaptive fading factors based on the orthogonality principle, which can effectively track the parameters’ varying. The second characteristic of ASRRLS is taking covariance square root matrix instead of covariance one in the filter algorithm, which can avoid filtering divergence and improve the filter convergence speed. The ASRRLS filter algorithm is deduced and the performance is rigorously analyzed. Simulation results demonstrate the feasibility and effectiveness of the proposed approach.
Yunjie Qu Wenhui Wang Lu Zhang Bangqing Li Kai Yu
Beijing Institute of Automatic Control Equipment,Beijing,China. Beijing Institute of Automatic Control Equipment,Beijing,China School of Astronautic,Harbin Institute of Technology,Harbin,China.
国际会议
哈尔滨
英文
1145-1148
2010-01-08（万方平台首次上网日期，不代表论文的发表时间）