Optimal Pre-filter Design for SINS based on Particle Swarm Optimization

L. Fu, J.Z. Li, L.L, Wang

Peer Reviewed

Abstract:	The long-term accuracy of strapdown inertial navigation system (SINS) generally cannot achieve an acceptable level for the high accuracy SINS. The common method to solve the problem is to improve the precision of the inertial sensor or the accuracy of the navigation algorithm. However, this paper focuses on the influence that the pre-filter applies on the accuracy of the SINS. The best influence can be obtained by adjusting the pre-filter. A novel approach is proposed to design an optimal pre-filter to match the four sample attitude algorithm for improving SINS accuracy. The pre-filter which is often used to remove the high frequency noise in the inertial sensors outputs has a shaped frequency characteristic of inertial sensors outputs. The characteristic leads to an influence on the accuracy of SINS. The different types and different parameters of pre-filters which shape the inertial sensor outputs differently have different influences on the accuracy of SINS. For designing an optimal pre-filter, the approach presented utilizes the particle swarm optimization (PSO) algorithm to find the optimal type and the optimal parameters for the optimal pre-filter design. The PSO algorithm is an intelligence algorithm widely used for the optimization of continuous nonlinear function .It can speed up the designing process owing to a fast convergence. This paper just chooses the finite impulse response (FIR) filter and the wavelet filter as the optimized objects to prove the feasibility of the approach. The optimal pre-filter design procedure is presented in detail. In order to analyze the influences of different types and different parameters of the pre-filter, the yaw error is regarded as the measurement of the influence. The PSO algorithm is used for the optimization of the parameters of FIR filter and wavelet filter. Because the coning error is affected by the pre-filter, it is used to construct the fitness function which is needed in the PSO algorithm to evaluate the parameters. The derivation of the fitness function is provided in this paper.
Published in:	Proceedings of the 27th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2014) September 8 - 12, 2014 Tampa Convention Center Tampa, Florida
Pages:	2056 - 2061
Cite this article:	Fu, L., Li, J.Z., L.L,, Wang,, "Optimal Pre-filter Design for SINS based on Particle Swarm Optimization," Proceedings of the 27th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2014), Tampa, Florida, September 2014, pp. 2056-2061.
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