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| Citation: | 
|
Track-Before-Detect Algorithm Based on Improved Auxiliary Particle PHD Filter under Clutter Background
DOI: 10.12000/JR18060
More Information-
Abstract
Under the clutter background condition, the existing particle filter pre-detection tracking algorithm based on Probability Hypothesis Density (PHD) filtering is not accurate enough to estimate the number of targets in dense multi-objectives. In this study, the concept of two-layer particle is introduced. The Auxiliary Particle Filter (APF) based on Parallel Partition (PP) theory is applied to PHD-TBD. The Auxiliary Parallel Partition Particle Filter (which is based on APF and PP) Track-Before-Detect based on the Probability Hypothesis Density filter (APP-PF-PHD-TBD) algorithm is proposed to improve the target number and state estimation accuracy. The simulation results show that, compared with the existing PHD-filtering-based particle filter track-before-detect algorithm, the proposed algorithm has significant performance advantages in target number and state estimation accuracy. These advantages are particularly obvious in dense target scenarios. Finally, the sea clutter background data obtained using the navigation radar prove that the proposed algorithm outperforms the existing PHD-filtering-based particle filter track-before-detect algorithm in application. -
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Du Kangning, Deng Yunkai, Wang Yu, Li Ning. Medium Resolution SAR Image Time-series Built-up Area Extraction Based on Multilayer Neural Network[J]. Journal of Radars, 2016, 5(4): 410-418. doi: 10.12000/JR16060
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- Figure 2. Exp.1 the performance of the two method in 8 dB
- Figure 1. Exp.1 the performance of the two method in 9 dB
- Figure 3. Exp.1 the performance of the two method in 6 dB
- Figure 5. Exp.2 the performance of the two method in 8 dB
- Figure 4. Exp.2 the performance of the two method in 9 dB
- Figure 6. Exp.2 the performance of the two method in 6 dB
- Figure 7. The data of the 20th scan after adding the targets
- Figure 8. The comparison of the detected targets number in Exp.3
- Figure 9. The comparison of the location accuracy in Exp. 3