Abstract
The fuzzy recursive least squares-probabilistic data association (FRLS-PDA) filter is presented for tracking single maneuvering target in cluttered situations with unknown process noises. In the proposed filter, the association probabilities of the current valid measurements belonging to a motion target are calculated by the probabilistic data association (PDA) algorithm. Then these probabilities are used to weight the valid measurements for generating a fused measurement, which is applied to determine the maneuvering characteristics of the moving target in real time including the current measurement residual and heading change. According to the above characteristics calculated, the fuzzy recursive least squares (FRLS) filter is used to estimate the current state of the target. The proposed filter can provide the advantage of the FRLS filter, which relaxes the restrictive assumptions of motion models of a maneuvering target. Moreover, it can realize single maneuvering target tracking in cluttered situations. The performance of the FRLS-PDA filter is evaluated by two experiments with the simulation data and real data, and it is found to be better than those of the PDA filter, IMM-PDA filter, fuzzy adaptive α-β filter, and FRLS filter in tracking accuracy.
Highlights
Due to the uncertainty in tracking process, single maneuvering target tracking (MTT) has become a difficult problem in information fusion, in clutter [1, 2]
Based on the above analysis, the fuzzy recursive least squares-probabilistic data association (FRLS-PDA) filter is proposed for single MTT in cluttered situations with unknown process noises
3 The proposed filter for MTT To track a single maneuvering target in clutter environments, this paper further proposes a FRLS-PDA filter based on the PDA algorithm and the FRLS filter
Summary
Due to the uncertainty in tracking process, single maneuvering target tracking (MTT) has become a difficult problem in information fusion, in clutter [1, 2]. In situations of unknown process noises, the recursive least squares (RLS) filter can obtain the perfect estimated results and possess less calculation complexity compared with the traditional Kalman filter. It is widely applied in target tracking, system identification, and automatic control, etc. The fuzzy recursive least squares (FRLS) filter is proposed for tracking single maneuvering target [28] It cannot be directly applied in cluttered situations. Based on the above analysis, the fuzzy recursive least squares-probabilistic data association (FRLS-PDA) filter is proposed for single MTT in cluttered situations with unknown process noises. Both H1 and H2 are an m × m measurement transition matrix, and P2 is an n × n filter covariance matrix
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
