Adaptive fault isolation and system reconfiguration method for GNSS/INS integration

Abstract

Appropriate fault isolation and system reconfiguration scheme is of great significance to ensure the reliability and precision of the tightly coupled global navigation satellite system/inertial navigation system (GNSS/INS). Currently, the commonly used methods include fault isolation method and fault adaptation method. However, the performance of them is not compared and analyzed fully in the existing literatures. In this paper, the principle of them is analyzed and the performance of them under different conditions is compared in theory firstly. On this basis, to improve the effectiveness and adaptability of fault detection and isolation, an adaptive fault isolation and system reconfiguration method is proposed. The radial basis function neural network (RBFNN) is used to predict the pseudo-GNSS measurement for the measurement reconfiguration. Besides, taking the variety of observation conditions into consideration, an adaptive adjustment criterion is introduced to realize the switch of fault isolation and measurement reconfiguration. The performance of the proposed method is verified and compared with the traditional methods by using the field test data. The results show that the fault isolation method can obtain higher filtering precision than the fault adaptation method in theory, compared with these two methods, the proposed method has better adaptability to the complex environment, and can improve the reliability and precision of the navigation system effectively.