The motivation of INS/GPS integration is to develop a navigation system that overcomes the shortcomings of each system. Its implementation is essentially based on the filter techniques and error models of INS. If the model changes with the environment, the estimation accuracy is degraded. In this paper, an Interacting Multiple Model Unscented Kalman Filter (IMM-UKF) method was proposed to jointly estimate the position information. This modeling approach makes it possible to employ the UKF to deal with the problem of nonlinear filtering with uncertainty noise. The output of the IMM-UKF is the weighted sum of a bank of parallel unscented Kalman filters. Simulations show that compared with the conventional Kalman filtering approach, the IMM-UKF algorithm is more stable and effective, thus improving the convergence speed and accuracy.

This paper presents a sequential filter implementation of particle Probability Hypothesis Density (PHD) filter for multisensor multi-target tracking. The tracking system involves potentially nonlinear target dynamics described by Markov state space model and nonlinear measurements. Each sensor reports measurements to the tracking system, which performs sequential estimation of the current state using the particle PHD filter, which propagates only the first order statistical moment of the full posterior of the multi-target state. Simulation results are also given and compared with a single radar multi-target tracking, showing the advantage of the fusion tracking over the single radar multi-target tracking. Keywords-random finite sets; particle PHD filter; multitarget tracking; sequential fusionI.978-1-4244-4994-1/09/$25.00 ©2009 IEEE

This paper proposes a practical algorithm for the reduction of measurement errors due to drift in Micro-ElectroMechanical System (MEMS) gyros which is used for mobile robot. Drift in MEMS gyros will cause the unbounded growth of errors in the estimation of yaw, which makes it nearly useless in applications that require good accuracy for longer time. The method used in this paper is called "Fuzzy Heuristic Drift Reduction" (FHDR). To verify the validity of the algorithm, the paper presents results of experiments, in which a gyro-equipped indoor mobile robot walked for several minutes. FHDR reduced the final heading error over all of these drives by one order of magnitude compared of nonuse.

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