A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking †
Abstract
:1. Introduction
2. Background
2.1. Notation
2.2. GLMB and LMB RFS
2.3. Multi-Target Bayes Forward–Backward Smoother
2.4. Multi-Target Motion and Measurement Models
3. LMB Smoother
3.1. Forward LMB Filtering
3.1.1. Prediction
3.1.2. Update
3.2. Backward LMB Smoothing
4. SMC Implementation and Algorithm Analysis
4.1. SMC Implementation
4.2. Backward Smoothing and State Extraction
Algorithm 1: The proposed backward LMB smoothing algorithm. |
Input: lag at time t, , ; |
initialize with ; |
for k=t:-1:max(,1)+1 |
; |
for q = 1:size(,2) |
compute according to (33); |
for j=1: |
estimate according to (51); |
end |
for i=1: |
compute according to (48)–(49); |
; |
end |
end |
end |
Output: . |
4.3. Algorithm Complexity
5. Simulation Result
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
Appendix B
Appendix C
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Method | Total | Localization | Cardinality |
---|---|---|---|
OSPA (m) | Component (m) | Component (m) | |
PHD filter | 34.3821 | 26.4094 | 7.9727 |
PHD smoother | 27.3199 | 18.6526 | 8.6673 |
CBMeMBer filter | 30.2842 | 22.3566 | 7.9276 |
MeMBer smoother | 22.0721 | 14.6664 | 7.4056 |
LMB filter | 25.6800 | 22.6574 | 3.0226 |
LMB smoother | 15.1762 | 14.4932 | 0.6830 |
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Liu, R.; Fan, H.; Li, T.; Xiao, H. A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking. Sensors 2019, 19, 4226. https://doi.org/10.3390/s19194226
Liu R, Fan H, Li T, Xiao H. A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking. Sensors. 2019; 19(19):4226. https://doi.org/10.3390/s19194226
Chicago/Turabian StyleLiu, Rang, Hongqi Fan, Tiancheng Li, and Huaitie Xiao. 2019. "A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking" Sensors 19, no. 19: 4226. https://doi.org/10.3390/s19194226