Science and Technology on Electro Optic Control Laboratory of Luoyang

and Technology on, China

Science and Technology on Electro Optic Control Laboratory of Luoyang

and Technology on, China
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Zhan K.,Beihang University | Xu L.,Beihang University | Jiang H.,Beihang University | Jiang H.,Science and Technology on Electro Optic Control Laboratory of Luoyang | And 2 more authors.
2014 IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2014 | Year: 2015

To overcome the high computational complexity of the existing joint tracking and classification (JTC) algorithm, particle filter (PF) is introduced to replace numerical integration in solving JTC, and hence computational load is considerably reduced. Our particle filter based JTC (PF-JTC) algorithm makes use of the target kinematic information provided by the low-resolution radar (LLR) and the target electromagnetic equipment information provided by the electronic support measure (ESM) to improve the performance of tracking and classification simultaneously. Simulation results verify the effectiveness of the proposed PF-JTC algorithm. © 2014 IEEE.


Xu L.,Beihang University | Zhan K.,Beihang University | Jiang H.,Beihang University | Jiang H.,Science and Technology on Electro Optic Control Laboratory of Luoyang | And 2 more authors.
2014 IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2014 | Year: 2015

For the ground-based passive radar to monitor low altitude threat targets, the radio frequency modulation (FM) signals transmitted by the broadcast stations is exploited, and an effective joint tracking and classification (JTC) algorithm based on aerodynamic model and radar cross section (RCS) is presented. The aerodynamic equations are used as motion model, and target classification is made possible by the inclusion of RCS in the measurement vector. Thus, tracking and classification are closely coupled, giving full play to the advantages of joint tracking and classification. Our algorithm is implemented by interacting multiple model regularized particle filter (IMMRPF) and simulations show the superiority of our algorithm. © 2014 IEEE.

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