Proposed robust auxiliary particle filtering for navigation system

In nonlinear and non-Gaussian systems, particle filtering is effective but it diverges and causes degeneration when the measurement precision is high, and it is difficult to select the importance distribution function. We present an improved robust auxiliary particle filtering algorithm to improve the filtering performance. By using the equivalent weight and taking advantage of the high efficiency of particle filtering, the algorithm is applied auxiliary particle filtering to generate mean and variance, and it constructs equivalent weight that makes good use of more reasonable information. Then we renew and establish importance distribution function that considers the latest measured values and slows down the particle degradation in importance sampling process. Particles are from the continuous kernel density distribution function owned the minimum mean square error in resampling process. Simulation results show that the improved robust auxiliary particle filtering can reduce the errors of navigation position based on GPS/DR vehicle integrated navigation, and outperform the standard particle filtering in terms of accuracy.