Abstract
Particle filter has been an efficient tool in many areas, how to improve the sampling efficiency of particle filter and the capability of an algorithm to track an abrupt change state is the key to the research and application of particle filter. This paper combines the strong tracking filter with the particle filter, and proposes the improved strong tracking particle filtering (ISTPF) algorithm. The algorithm uses strong tracking Kalman filter to generate a correction term and uses it to correct the sample particles during particle filtering, which greatly improves sampling efficiency and resolves the problem of particle degeneracy and low sampling efficiency in the traditional strong tracking particle filtering algorithms. Finally, the ISTPF algorithm and the other two strong tracking filters STUKF and STEPF are applied to the satellite attitude estimation which is described by multiplicative error quaternion under the same conditions, and the simulation results demonstrate that ISTPF has the highest precision, which indicating that the ISTPF is feasible and superior.
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