Multipath Estimation of Navigation Signals Based on Extended Kalman Filter–Genetic Algorithm Particle Filter Algorithm

The particle filter (PF) algorithm has found widespread application in navigation multipath estimation. However, it exhibits significant limitations in complex multipath environments. Its state prediction relies heavily on particle distribution and is prone to particle degeneracy, where the weights of most particles approach zero, and only a few particles contribute significantly to state estimation. These issues result in an inadequate number of effective samples, degrading multipath estimation performance. Therefore, a navigation multipath estimation method based on an EKF-GAPF (Extended Kalman Filter–Genetic Algorithm Particle Filter) algorithm is proposed in this paper. This method utilizes the EKF to calculate the mean and covariance of samples using the latest observation information, providing a more reasonable proposal density for particle filtering and enhancing the accuracy of state prediction. Simultaneously, by introducing the crossover and mutation mechanisms of the adaptive genetic algorithm, particles are continuously evolved during the resampling process, preventing them from falling into local extrema. Experimental results show that EKF-GAPF outperforms EKF, EPF, and PF in amplitude and delay estimation. Under the condition of random initial values, the multipath signal amplitude estimation error converges to 0.002, and the multipath signal time delay estimation error converges to 0.006 (approximately 1.8 m). This method enables high-precision parameter estimation for both direct and multipath signals.