Controlling particle filter regularization for GPS/INS hybridization

Coupling GPS with Inertial Navigation Systems (INS) is an interesting way of improving navigation performance in terms of accuracy and continuity of service. This coupling is generally performed by using GPS pseudorange measurements to estimate INS estimation errors and sensor biases. Particle filtering techniques are good candidates to solve the corresponding estimation problem due to the nonlinear measurement equation. However, classical particle filter algorithms tends to degenerate for this application because of the small state noise. Regularized particle filters allow to overcome this limitation at the expense of noisy state estimates. A recent regularized particle filter was proposed to control the regularization process by a Metropolis-Hasting step. The method was shown to increase particle filter robustness while decreasing the variance of the estimates. This paper goes further by introducing an appropriate criterion which measures the degeneracy of the particle cloud. This criterion is used to control the regularization which is not applied systematically reducing the algorithm computational cost. The main idea of the proposed strategy is to monitor on line the mean jumps of the predicted measurement likelihood by means of a CUSUM algorithm. Simulation results are proposed to validate the relevance of the criterion and the performance of the overall algorithm.