Initial Alignment Error On-Line Identification Based on Adaptive Particle Swarm Optimization Algorithm

Weilin Guo,Leliang Ren,Bing Li,Yong Xian

doi:10.1155/2018/3486492

Weilin Guo, Leliang Ren + Show 2 more

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https://doi.org/10.1155/2018/3486492

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Abstract

To solve the problem of high accuracy initial alignment of strap-down inertial navigation system (SINS) for ballistic missile, an on-line identification method of initial alignment error based on adaptive particle swarm optimization (PSO) is proposed. Firstly, a complete navigation model of SINS is established to provide the accurate model basis for subsequent numerical optimization calculation. Then setting the initial alignment error as the optimization parameter and regarding the minimum deviation between SINS and GPS output as the objective function, the error parameter optimization model is designed. At the same time, the mutation idea of genetic algorithm (GA) is introduced into the PSO; thus the adaptive PSO is adopted to identify the initial alignment error on-line. The simulation results show that it is feasible to solve the initial alignment error identification problem of SINS by intelligent optimization algorithm. Compared with the standard PSO algorithm and the GA, the adaptive PSO algorithm has the fastest convergence speed and the highest convergence precision, and the initial pitch error and the initial yaw error precision are within 10′′ and the initial azimuth error precision is within 25′′. The navigation accuracy of SINS is improved effectively. Finally, the feasibility of the adaptive PSO algorithm to identify the initial alignment error is further validated based on the test data.

Highlights

Initial alignment for strap-down inertial navigation system (SINS) plays an important role in the navigation operation of the ballistic missile
A fast SINS initial alignment scheme based on the disturbance observer and Kalman filter is proposed to estimate the misalignment angles in [4], and an adaptive extended Kalman filter algorithm combined with innovation-based adaptive estimation is proposed in [7], while these filtering algorithms often have some disadvantages, such as the difficulty of model establishing, poor observability of parameters, and long alignment time [8]
The standard particle swarm optimization (PSO) is improved to the adaptive PSO, and the improved strategy is as follows: (1) The mutation idea is introduced into PSO algorithm based on genetic algorithm (GA) algorithm

Summary

Introduction

Initial alignment for strap-down inertial navigation system (SINS) plays an important role in the navigation operation of the ballistic missile. Improving the initial alignment accuracy of SINS is of great significance to improve the performance of ballistic missile weapon. The propagation process of the initial alignment error of SINS is a complex nonlinear problem. This paper rejects the traditional research method based on analytic simplification, linearization and filtering, attempting to convert the initial alignment problem of SINS into parameter optimization identification problem. The complete nonlinear optimization model is established, and the intelligent optimization algorithm is used to realize the on-line identification of the initial alignment error of SINS

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Results

Conclusion