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| A Multi-frame Association Dynamic Programming Track-before-detect Algorithm Based on Second Order Markov Target State Model |
| Zheng Dai-kun Wang Shou-yong Yang Jun Du Peng-fei |
| Air Force Radar Academy, Wuhan 430019, China |
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Abstract Traditional Dynamic Programming Track-Before-Detect (DP-TBD) algorithms use only observation data of current frame to associate with merit function and accumulate energy at each stage of data association. The ignorance of target’s state relevance among successive frames and its own kinematic characters results in false state association at low Signal-to-Noise Ratio (SNR), which reduce detecting and tracking performance profoundly. To solve this issue, a DP-TBD algorithm based on second order Markov target state model is proposed. Taking maximum of the target’s state conditional PDF ratio as the optimal criteria, this algorithm makes use of second order Markov model to describe the target’s state relevance and defines a state transition probability model according to target’s kinematic characters, which relates to target’s turning angle. On these bases, a multi-frame data association DP-TBD algorithm is implemented. Compared to traditional DP-TBD algorithm through a simulation experiment, the proposed algorithm turns out to have better detection and tracking performance.
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Received: 06 July 2011
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Corresponding Authors:
Zheng Dai-kun
E-mail: zheng_af@163.com
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2012, Vol. 34 
