Year: 2020
Type: article
Abstract: In this paper, an improved interactive multiple model adaptive strong tracking random weighted cubature Kalman filter (IIMM-ASTRWCKF) algorithm is developed to overcome the low tracking accuracy and easy divergence when dealing with complex maneuvering situations. The algorithm is improved in two aspects: On the one hand, ASTRWCKF is used as the sub filter of IMM algorithm to filter different motion models. By introducing the random weight factor to replace the original weight factor, the accuracy of the algorithm is improved. At the same time, the adaptive strong tracking filter is added to update the prediction covariance matrix and noise covariance matrix for the stability of the algorithm. On the other hand, this algorithm proposes a new method to improve the probability conversion accuracy of IMM by adding time-varying factor to adjust Markov probability transfer matrix. Compared with the performance of IMM-CKF and in dealing with maneuvering problems, IIMM-ASTRWCKF algorithm has better tracking accuracy in solving maneuvering problem.
Source: IEEE Access
Authors: Jian Ma, Xiaoting Guo
Institution: North University of China
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,750
Funder: North University of China
Award: G7179728989
OpenAlex ID: W3046957874