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| Citation: | Li Yangyang, Li Wen, Yi Wei, Kong Lingjiang. A Distributed Asynchronous Recursive Filtering Fusion Algorithm via DP-TBD[J]. Journal of Radars, 2018, 7(2): 254-262. doi: 10.12000/JR17057
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A Distributed Asynchronous Recursive Filtering Fusion Algorithm via DP-TBD
doi: 10.12000/JR17057
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Abstract
In this paper, we address target tracking problems by the use of multiple sensors via the Dynamic Programming (DP)-based Track-Before-Detect (TBD) method. Generally, DP-TBD is a grid-based method that estimates target trajectories by searching all the physically admissible paths in a determinate discrete state space. However, this multi-frame detection algorithm provides plot sequences without filtering or smoothing. With the growing complexity of the battle field environment, single radar based on DP-TBD cannot achieve satisfactory results when the Signal-to-Noise Ratio (SNR) is low. Besides, it is very difficult to fuse plot sequences from different radars because they contain no state error covariance matrix. Furthermore, various radars always contain asynchronous data due to the diversity of sampling times and communication delays. To alleviate these problems, we propose a distributed asynchronous recursive filtering fusion (Dynamic Programming Fuison, DPF) algorithm based on DP-TBD, which is divided into two steps. In the first step, we propose an iterative filter algorithm via DP-TBD. Then, we convert the asynchronous evaluation data into synchronous data and implement several distributed fusion algorithms to estimate the target state. Simulation results show that the proposed algorithm can correctly estimate target trajectories and significantly enhance tracking accuracy compared to solo radar. In addition, this algorithm can decrease the track loss rate and calculation burden. -
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References
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- Figure 1. The relationships of multiple sliding window frames when N=5
- Figure 2. The detail method flow of DPF algorithm
- Figure 3. Asynchronous data conversion figure
- Figure 4. One time Monte Carlo simulation result when SNR=8
- Figure 5. RMSE from the 1st frame to the 20nd frame
- Figure 6. Track loss rate of DPF algorithm and TDF method from SNR=8 dB to SNR=16 dB