Abstract

Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system’s error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the proposed fault tolerant fusion framework provides superior performance over its traditional counterparts.

Highlights

  • Recent developments in sensor technologies, telecommunication, and navigation systems made multi-sensor information fusion an indispensable component of the state-of-the-art integrated navigation systems

  • The paper develops an innovative multi-sensor fault-tolerant fusion framework applied in the Inertial Navigation System (INS)/Global Positioning System (GPS)/BD2/Celestial Navigation System (CNS) integrated navigation systems

  • The state estimates obtained from the prediction step of the Interacting Multiple Model (IMM) filter, without the incorporation of the measurement update, are used as fault detection references

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Summary

Introduction

Recent developments in sensor technologies, telecommunication, and navigation systems made multi-sensor information fusion an indispensable component of the state-of-the-art integrated navigation systems. The paper focuses on these problems and proposes an innovative fault tolerant multi-sensor information fusion framework applied in the INS/GPS/BD2/CNS integrated navigation system for improving the accuracy and reliability of such systems. To the best of our knowledge, few of these aforementioned methodologies can detect multi-failures at the same time effectively The paper addresses this gap and develops an improved multi-sensor fault tolerant fusion framework applied in the INS/GPS/BD2/CNS integrated navigation system. In this paper and to improve the reliability and fault-tolerant capability of the aircraft navigation system, we propose an innovative fault-tolerant integrated navigation framework via fusion of SINS, GPS, BD2, and CNS sensors. To address the problem of incorporating corrupted state propagators for fault detection purposes, the paper designs two state propagators’ chi-square fault detection procedure for the SINS/GPS/BD2/CNS integrated navigation system.

Multi-Sensor System Architecture
Failure-Tolerant Integrated Navigation Framework
Local IMM-KF Estimation Algorithm
Failure Detection Methodology
Information Fusion Framework
Results
Scenario 1
Scenario 2
Conclusions
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