Tracking and Identification for GPS/DR Integrated Navigation System with Unknown Parameters

This paper propses a filtering method for GPS/DR (Global Positioning System/Dead-Reckoning) integrated navigation system with unknown parameters. This method firstly structures a self-organizing state space model, and then estimates the state vector by using Monte Carlo filtering method for this new system model. Because particle filter is easy to make a search of the unknown parameters into a subset of the initial sampling for the self-organization model an artificial fish swarm-partical filter algorithm is put forward. The algorithm not only can estimate the system state, but also can make the sampling distribution of the unknown parameters move to the true parameter distribution. Ultimately, the true value of the unknown parameters are identified. The simuliation results show the effectiveness of the proposed method.