Error-State Kalman Filtering with Linearized State Constraints

Abstract:

This paper presents a fault tolerant navigation algorithm for vertical takeoff and landing advanced aerial mobility platforms. The proposed algorithm incorporates constraints based on the distances between sensors, using the innovation calculated from filter estimation and measurement as a weighting factor. With the proposed sensor configuration, the platform’s attitude is computed and then used for measurement updates, while an adaptive rule is employed to enhance filter robustness. A comprehensive analysis was conducted using Monte Carlo simulations to confirm the algorithm’s robustness, and experimental test verified its feasibility in real-world conditions. The results demonstrated that the proposed algorithm maintained robust performance even in various sensor fault scenarios and achieved significant improvements in attitude estimation accuracy compared to traditional navigation systems. Moreover, the successful experimental validation shows that the algorithm can be effectively implemented using a single inertial measurement unit together with multi-positioning sensors, such as real-time kinematics and ultra- wideband, thereby enhancing reliability and fault tolerance in aerial navigation applications.