Publications (2)0 Total impact
- [Show abstract] [Hide abstract]
ABSTRACT: The error model is nonlinear when the azimuth angle of strapdown inertial navigation system (SINS) on stable base is large, and a new filter results from using unscented Kalman filter for proposal distribution generation imbedding latest observed measurements in importance sampling step, and combining Gaussian mixture model and weighted expectation maximization (EM) algorithm to replace the traditional resampling step. And the "sample depletion" problem was lessened. It is demonstrated by simulation that this new approach has an improved estimation performance in initial alignment of large azimuth misalignment on static base of SINS.
Conference Paper: Application of an improved particle filter for state estimation[Show abstract] [Hide abstract]
ABSTRACT: A novel Gaussian mixture Sigma-Point Particle Filter algorithm is proposed to mitigate the sample depletion problem. The posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted Expectation-Maximization algorithm. The simulation results demonstrate the validity of the proposed algorithm.