Multisensor Multi-Target Tracking Based on GM-PHD Using Out-Of-Sequence Measurements
Abstract
:1. Introduction
2. Problem Formulation
2.1. System Model
2.2. OOSM B1 One-Step-Lag Algorithm
2.3. GM-PHD Filter
3. Multi-Target OOSM Tracking Algorithm
3.1. Backward State Prediction
Algorithm 1 Backward state prediction |
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3.2. Re-Update with OOSM
Algorithm 2 Re-update using OOSM |
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3.3. Weights Correction
Algorithm 3 Weight correction |
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Algorithm 4 RFS-based multi-target tracking using OOSM |
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4. Simulations
4.1. Distance
4.2. Compared Algorithm
4.3. Simulations Results
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
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Algorithm | ISM | OOSM | Frame Drop | |
---|---|---|---|---|
OSPA Distance (m) | ||||
clutter = 0 | 4.24 | 4.31 | 4.82 | |
clutter = 4 | 6.01 | 5.23 | 9.41 |
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Liu, M.; Huai, T.; Zheng, R.; Zhang, S. Multisensor Multi-Target Tracking Based on GM-PHD Using Out-Of-Sequence Measurements. Sensors 2019, 19, 4315. https://doi.org/10.3390/s19194315
Liu M, Huai T, Zheng R, Zhang S. Multisensor Multi-Target Tracking Based on GM-PHD Using Out-Of-Sequence Measurements. Sensors. 2019; 19(19):4315. https://doi.org/10.3390/s19194315
Chicago/Turabian StyleLiu, Meiqin, Tianyi Huai, Ronghao Zheng, and Senlin Zhang. 2019. "Multisensor Multi-Target Tracking Based on GM-PHD Using Out-Of-Sequence Measurements" Sensors 19, no. 19: 4315. https://doi.org/10.3390/s19194315