Residual-Based Fault Detection and Exclusion With Enhanced Localization Integrity

In this paper, we give an integrity framework for vehicle localization in urban environments based on fusion of GPS and dead reckoning. To guarantee localization integrity, a novel Kalman filter-based fault detection and exclusion method is presented to reject faulty measurements via a chi-square test with iterative residuals. For a tight upper-bound for position error, we introduce a protection level calculation method with minimum detectable bias and the integrity budget allocation among all failure modes have been improved to enhance the accuracy of positioning. The algorithm provides integrity assessment and reliable estimations of vehicle position information in an iterative manner. Numerical simulations and experiments show that the proposed method can effectively deal with and recover from abrupt faults with a low rate of false alarm and a tight integrity risk bound.