Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network
Abstract
:1. Introduction
2. Mathematical Background
2.1. Labeled RFS and Mδ-GLMB
2.2. Information Inequality to RFS Measurement
2.3. A New Metric for Labeled RFS
3. Problem Formulation
Algorithm 1. Sensor selection and MTT for the LFC . |
1. Prediction: Calculate the current predicted density by , where is the fused density at the last time; |
2. SN selection: Select the SN subset and receive a collection of their measurement sets ; |
3. Update: Calculate the current posterior density by and then transmit the density to the LFCs connected to the LFC ; |
4. Fusion: Receive the posterior densities from the LFC set and then calculate the fused density by the weighted KLA rule , where () is the preset normalized weight; |
5. State extraction: Extract the current state estimate from as the output. Go to Step 1. |
4. Lower Bound For LA Metric Based MSE and Sub-Optimization For Sensor Selection
4.1. Derivation of LA Bound
Algorithm 2. Steps for calculating and . |
1. Prediction sampling: Generate samples of multi-target state sets from the predicted density ; |
2. PIMS generating: For , generate PIMS of the SN set based on [31]; |
3. PIMS partitioning: Divide PIMS into the measurement subspace () according to (30); |
4. MC integration: Given the PIMS assigned to , and are obtained by applying MC integral formula [45] to (39) and (42). |
4.2. SMC and GM Implementations for the Bound
4.3. Sub-Optimization Based on Coordinate Descent
Algorithm 3. Coordinate descent method. |
Step 1: Set initial iteration number , initial SN switch vector , initial barrier factor and its reduction coefficient ; |
Step 2: From to , calculate , where are treated as constants; |
Step 3: If , then go to Step 4; Otherwise, set , go to Step 2; |
Step 4: If , then output as the solution of (17); Otherwise, set , , and then go to Step 2. |
4.4. Weighted KLA Fusion
5. Simulations
6. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
Appendix A.
Appendix B.
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Clutter rate and Detection Probability | ||||||
---|---|---|---|---|---|---|
Sensor Selection Method | ||||||
LA bound with coordinate descent | 171.3 | 193.0 | 218.9 | 249.1 | 283.7 | |
LA bound with genetic algorithm | 151.9 | 170.8 | 193.6 | 220.5 | 251.6 | |
CS divergence with genetic algorithm | 238.6 | 278.5 | 333.7 | 405.3 | 494.4 | |
Random selection | 386.7 | 436.0 | 490.5 | 550.1 | 615.2 |
Clutter rate and Detection Probability | ||||||
---|---|---|---|---|---|---|
Sensor Selection Method | ||||||
LA bound with coordinate descent | 188.8 | 223.9 | 255.0 | 288.6 | 323.9 | |
LA bound with genetic algorithm | 165.7 | 199.6 | 229.5 | 262.9 | 292.3 | |
CS divergence with genetic algorithm | 260.1 | 321.2 | 383.9 | 463.9 | 562.8 | |
Random selection | 426.7 | 500.5 | 564.3 | 634.2 | 710.9 |
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Lian, F.; Hou, L.; Wei, B.; Han, C. Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network. Sensors 2018, 18, 4115. https://doi.org/10.3390/s18124115
Lian F, Hou L, Wei B, Han C. Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network. Sensors. 2018; 18(12):4115. https://doi.org/10.3390/s18124115
Chicago/Turabian StyleLian, Feng, Liming Hou, Bo Wei, and Chongzhao Han. 2018. "Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network" Sensors 18, no. 12: 4115. https://doi.org/10.3390/s18124115
APA StyleLian, F., Hou, L., Wei, B., & Han, C. (2018). Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network. Sensors, 18(12), 4115. https://doi.org/10.3390/s18124115