A new RAIM algorithm based on Gauss particle filter (GPF-RAIM) is proposed in this paper for the degraded performance of extended Kalman filter RAIM (EKF-RAIM) under strong nonlinearity. GPF-RAIM uses Gauss particles to approximate estimation of non-linear state, and generates a new set of particles according to Gauss distribution in recursion. GPF-RAIM can solve the problem of particle degradation, and does not need to resample step to keep the diversity of particles. The simulation results show that GPF-RAIM can detect pseudo-range jump effectively. Compared with EKF-RAIM, GPF-RAIM can get smaller state estimation error and better detection performance.