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- Title
Gaussian mixture probability hypothesis density filter for multipath multitarget tracking in over-the-horizon radar.
- Authors
Qin, Yong; Ma, Hong; Chen, Jinfeng; Cheng, Li
- Abstract
Conventional multitarget tracking systems presume that each target can produce at most one measurement per scan. Due to the multiple ionospheric propagation paths in over-the-horizon radar (OTHR), this assumption is not valid. To solve this problem, this paper proposes a novel tracking algorithm based on the theory of finite set statistics (FISST) called the multipath probability hypothesis density (MP-PHD) filter in cluttered environments. First, the FISST is used to derive the update equation, and then Gaussian mixture (GM) is introduced to derive the closed-form solution of the MP-PHD filter. Moreover, the extended Kalman filter (EKF) is presented to deal with the nonlinear problem of the measurement model in OTHR. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.
- Subjects
GAUSSIAN mixture models; OVER-the-horizon radar; KALMAN filtering
- Publication
EURASIP Journal on Advances in Signal Processing, 2015, Vol 2015, Issue 1, p1
- ISSN
1687-6172
- Publication type
Article
- DOI
10.1186/s13634-015-0294-y