An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer

Yuming Chen,Hai Yang,Ting Xia,Wei Li,Gaifang Xin

doi:10.1155/2019/3172501

Abstract

The strap-down inertial navigation system (SINS) is a commonly used sensor for autonomous underground navigation, which can be used for shearer positioning under a coal mine. During the process of initial alignment, inaccurate or time-varying noise covariance matrices will significantly degrade the accuracy of the initial alignment of the shearer. To overcome the performance degradation of the existing initial alignment algorithm under complex underground environment, a novel adaptive filtering algorithm is proposed by the integration of the strong tracking Kalman filter and the sequential filter for the initial alignment of the shearer with complex underground environment. Compared with the traditional multiple fading factor strong tracking Kalman filter (MSTKF) method, the proposed MSTSKF algorithm integrates the advantage of strong tracking Kalman filter and sequential filter, and multiple fading factor and forgetting factor for east and north velocity measurement are designed in the algorithm, respectively, which can effectively weaken the coupling relationship between the different states and increase strong robustness against process uncertainties. The simulation and experiment results show that the proposed MSTSKF method has better initial alignment accuracy and robustness than existing strong tracking Kalman filter algorithm.

Highlights

Coal is the most widely distributed and abundant energy resources in the world, and it has become one dominant energy in the world energy architecture [1]
This paper investigates the initial alignment for the strap-down inertial navigation system (SINS) under the complex environment of the mechanized mining face
Aiming at the problems of slow convergence and poor accuracy for the existing Kalman filter (KF) algorithm under the complex environment of the mechanized mining face, this paper systematically proposes MSTSKF method to suppress inaccurate or time-varying noise statistics induced by harsh environment

Summary

Introduction

Coal is the most widely distributed and abundant energy resources in the world, and it has become one dominant energy in the world energy architecture [1]. Strong tracking Kalman filter (STKF) is an AKF that aiming at the unknown statistical characteristics of noise based on the covariance matching technique [29, 30] This filter is introducing a time-varying suboptimal fading factor into the prediction covariance to enhance the state estimation. It is difficult to deal with high-dimensional filter problem since STKF cannot guarantee the effectiveness of each state, and even leads to the divergence of the filter To solve this problem, multiple fading factor strong tracking Kalman filter (MSTKF) is proposed to improve the filter performance for the highdimensional system. In view of the above problems, this paper presents a new strong tracking Kalman filter based on the concept of sequential filter to degrade the coupling relationship between each state, while improving the accuracy and robustness of the initial alignment under complex environment.

Initial Alignment of the Shearer Based on Improved STKF Algorithm

Performance Evaluation

13: Outputs

Experimental Verification

Conclusions