Building a robust integrity monitoring algorithm for a low cost GPS-aided-INS system

In this paper, the integrity of low cost GPS/INS systems is investigated to ensure the ability to obtain continuous high-integrity, high-accuracy vehicle state estimate under low-computational system requirement. The utilization of two fault detection and identification (FDI) techniques, the χ2 (or sometimes referred to as chi-squared) gating function and the multiple model adaptive estimation (MMAE), is proposed to monitor the integrity of GPS measurements. A fault in GPS measurements is modeled with an increase in GPS measurements noise covariance matrix which may result from mistuning of filter’s noise parameters, interference, jamming, or multipath errors. These types of faults are covered by this work and are assumed to last for unconstrained period of time. ξ2 FDI systems are computationally very inexpensive, have good fault detection ability and require no a priori knowledge on system dynamics. However, they are sensitive to filter tuning and fail to detect faults when the filter converges to them rather than rejecting them. Model-based approaches provide outstanding FDI ability. However, they are computationally demanding, require a priori knowledge on system model, sensitive to mismodeling errors, have finite convergence time, and compromise filter optimality under no-failure conditions. The proposed fusion algorithm guarantees integrity and does not affect filter’s optimality under no-failure conditions. Simulated and experimental tests were conducted to verify the accuracy of the proposed techniques. Results are presented at the end of the paper to highlight the performance characteristics of the proposed FDI system implementation.