Particle RAIM

We propose a novel particle filter-based framework for robust state estimation and integrity monitoring in urban environments using GNSS and odometry measurements. Using a Gaussian mixture model (GMM) for computing particle weights, we develop an expectation-maximization algorithm for jointly inferring GMM weight parameters and a robust particle distribution of receiver state. From the inferred particle distribution and GMM, we determine the navigation system availability based on specified integrity requirements. Unlike traditional residual-based integrity monitoring algorithms that analyze measurement residuals from an estimated receiver position, we incorporate measurement residuals from multiple particles to estimate the receiver position and monitor integrity. Our method achieves small horizontal positioning errors compared to existing filter-based state estimation techniques on challenging simulated and real urban driving scenarios with multiple erroneous measurements. Through multiple simulations, we also show that our method determines system availability with comparable false alarms and missed identifications to best-performing existing integrity monitoring approaches.