Optimization Algorithm for Kalman Filter Exploiting the Numerical Characteristics of SINS/GPS Integrated Navigation Systems

Sensors

Shaoxing Hu¹  Shike Xu¹  Duhu Wang¹  Aiwu Zhang² 

[1] School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China; E-Mails:;Ministry of Education Key Laboratory of 3D Information Acquisition and Application, Capital Normal University, Beijing100089, China; E-Mail:

关键词: computational optimization;    SINS/GPS;    closed-loop Kalman filter;    block matrix;    offline-derivation;    parallel processing;    accuracy-lossless decoupling;    symbol operation;   

DOI  :  10.3390/s151128402

来源: mdpi

【 摘 要 】

Aiming at addressing the problem of high computational cost of the traditional Kalman filter in SINS/GPS, a practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented in this paper. The algorithm exploits the sparseness and/or symmetry of matrices to simplify the computational procedure. Thus plenty of invalid operations can be avoided by offline derivation using a block matrix technique. For enhanced efficiency, a new parallel computational mechanism is established by subdividing and restructuring calculation processes after analyzing the extracted “useful” data. As a result, the algorithm saves about 90% of the CPU processing time and 66% of the memory usage needed in a classical Kalman filter. Meanwhile, the method as a numerical approach needs no precise-loss transformation/approximation of system modules and the accuracy suffers little in comparison with the filter before computational optimization. Furthermore, since no complicated matrix theories are needed, the algorithm can be easily transplanted into other modified filters as a secondary optimization method to achieve further efficiency.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.