Node Selection and Path Optimization for Passive Target Localization via UAVs
Abstract
:1. Introduction
1.1. Overview
1.2. Original Contributions
1.3. Organization
2. Problem Formulation
2.1. Target Localization Model Based on the Chan-TDOA Algorithm
2.2. CRLB Derivation for TDOA Measurement Errors in the Chan-TDOA Model
2.3. Optimization Problem Formation
3. CRLB-Based Node Selection Method
3.1. Problem Description
3.2. Constraint Analysis
3.3. Node Selection Optimization Model
3.4. Node Selection Algorithm Design
Algorithm 1: CRLB-based node selection algorithm |
4. CRLB-Based Path Optimization Method
4.1. Problem Description
4.2. Constraint Analysis
4.3. Path Optimization Model
4.4. Path Optimization Algorithm Design
Algorithm 2: CRLB-based path optimization via PSO |
5. Numerical Results
5.1. Simulation Parameters and the Topology Structure Set
5.2. Simulation Results of the Node Selection Algorithm
5.3. Simulation Results of the CRLB-Based Path Optimization Method
5.4. Impact Analysis of Minimum Turning Radius
5.5. Impact Analysis of No-Fly-Zone Size
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Mathematical Derivations of Chan-TDOA Algorithm
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RMSE (Traditional) (m) | RMSE (Proposed) (m) | Percentage Reduction (%) |
---|---|---|
564.7680 | 352.0307 | 37.6681 |
532.7729 | 408.3508 | 23.3537 |
590.6810 | 427.4778 | 27.6297 |
Parameter | PSO | GA | ACO |
---|---|---|---|
Size | 50 particles | 20 individuals | 50 ants |
Max iterations | 500 | 500 | 500 |
Early stopping | Fitness < | No improvement in | fitness < |
criterion | 20 generations | ||
Special | : 2.5 → 0.5 | Crossover rate: 0.8 | Evaporation rate: 0.1 |
parameters | : 0.5 → 2.5 | Mutation rate: 0.1 | Influence factor (): 1 |
w: 0.9 → 0.4 | Elite count: 2 | Heuristic factor (): 2 | |
Constriction: 0.729 | Deposit factor (Q): 1 | ||
Optimization | Local search per | Elite retention | Local search per |
strategy | 10 iterations; | 10 iterations; | |
Reinitialize 10% | Space discretization: | ||
Particles per 20 iter. | 50 points |
Metric | Exhaustive | PSO | ACO | GA |
---|---|---|---|---|
CRLB reduction (%) | 66.70 | 66.66 | 66.77 | 66.57 |
RMSE reduction (%) | 44.24 | 44.99 | 45.02 | 44.64 |
Single iteration computation time(s) | 4.28 | 1.34 | 3.05 | 7.76 |
Minimum Turning Radius | CRLB Reduction | RMSE Reduction |
---|---|---|
(%) | (%) | |
5000 m | 64.57 | 45.38 |
7500 m | 62.15 | 43.14 |
10,000 m | 61.71 | 41.79 |
No-Fly-Zone Radius | CRLB Reduction | RMSE Reduction |
---|---|---|
(%) | (%) | |
1000 m | 63.88 | 45.02 |
2000 m | 62.51 | 44.93 |
3000 m | 59.94 | 42.46 |
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Xing, X.; Zhong, Z.; Li, X.; Yue, Y. Node Selection and Path Optimization for Passive Target Localization via UAVs. Sensors 2025, 25, 780. https://doi.org/10.3390/s25030780
Xing X, Zhong Z, Li X, Yue Y. Node Selection and Path Optimization for Passive Target Localization via UAVs. Sensors. 2025; 25(3):780. https://doi.org/10.3390/s25030780
Chicago/Turabian StyleXing, Xiaoyou, Zhiwen Zhong, Xueting Li, and Yiyang Yue. 2025. "Node Selection and Path Optimization for Passive Target Localization via UAVs" Sensors 25, no. 3: 780. https://doi.org/10.3390/s25030780
APA StyleXing, X., Zhong, Z., Li, X., & Yue, Y. (2025). Node Selection and Path Optimization for Passive Target Localization via UAVs. Sensors, 25(3), 780. https://doi.org/10.3390/s25030780