Positioning Error Analysis of Distributed Random Array Based on Unmanned Aerial Vehicles in Collaborative Jamming
Abstract
1. Introduction
- A calculation method for the jamming gain is proposed based on the pattern multiplication theorem and radiation intensity, further researching the power propagation in the target area, no longer limited to the characteristics of the beam itself.
- The target azimuth JSR and effective jamming range are defined as performance evaluation parameters based on collaborative jamming scenarios, which are more matched with jamming scenarios compared to analyzing the half-power beamwidth (HPBW) and power of the array’s main lobe.
- The trend of JSR and effective range changes at different jamming frequencies is simulated under various distribution radii and numbers of nodes.
- The upper bounds of azimuth and distance errors are provided in multiple collaborative jamming scenarios where the effective jamming probability reaches 90%.
- The inter-relationship between azimuth and distance errors is analyzed under different effective jamming probabilities, providing a reference for rationally selecting the number of nodes and deploying swarms.
2. The Far-Field Condition
3. Collaborative Jamming Model
4. Jamming Effectiveness Evaluation
4.1. Ideal Jamming Effectiveness
4.2. Imperfect Jamming Effectiveness
4.2.1. Jamming Performance with Azimuth Error
4.2.2. Jamming Performance with Distance Error
4.2.3. Joint Analysis of Positioning Errors
4.2.4. Jamming Performance in Different Environments
- (1)
- Wind speed
- (2)
- Rainfall intensity
- (3)
- External source of interference
5. Comprehensive Experiment
5.1. Simulation-Based Jamming Analysis in Realistic Scenarios
5.2. Comparison with Ground-Based Systems
6. Conclusions
Author Contributions
Funding
Data Availability Statement
DURC Statement
Conflicts of Interest
Appendix A. Proof of Equation (24)
Appendix B. Proof of Equation (26)
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Parameters | Data | Unit | |||
---|---|---|---|---|---|
1 | 3.1 | 4.2 | 5 | ||
0.22 | 0.25 | 0.28 | 0.35 | ||
100% | 87% | 58% | 34% | / | |
4.9 | 4.5 | 4.0 | 2.9 |
Parameters | Data | Unit | ||||
---|---|---|---|---|---|---|
433 | 840.5 | 1430 | 2400 | 5829 | MHz | |
7.2 | 4.09 | 2.55 | 1.34 | 0.28 | ||
0.29 | 0.15 | 0.09 | 0.05 | 0.02 | m |
Parameters | Data | Unit | |||
---|---|---|---|---|---|
N | 8 | 16 | 32 | 128 | / |
5.35 | 12.65 | 27.44 | 39.05 | ||
0.20 | 0.35 | 0.43 | 0.57 | m |
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Zhao, Y.; Li, L.; Hu, D.; Huang, Z.; Guo, X.; Ba, J.; Huang, H.; Yuan, S.; Zhou, J. Positioning Error Analysis of Distributed Random Array Based on Unmanned Aerial Vehicles in Collaborative Jamming. Drones 2025, 9, 234. https://doi.org/10.3390/drones9040234
Zhao Y, Li L, Hu D, Huang Z, Guo X, Ba J, Huang H, Yuan S, Zhou J. Positioning Error Analysis of Distributed Random Array Based on Unmanned Aerial Vehicles in Collaborative Jamming. Drones. 2025; 9(4):234. https://doi.org/10.3390/drones9040234
Chicago/Turabian StyleZhao, Yongjie, Longqing Li, Deming Hu, Zhiping Huang, Xiaojun Guo, Junhao Ba, Honghe Huang, Shudong Yuan, and Jing Zhou. 2025. "Positioning Error Analysis of Distributed Random Array Based on Unmanned Aerial Vehicles in Collaborative Jamming" Drones 9, no. 4: 234. https://doi.org/10.3390/drones9040234
APA StyleZhao, Y., Li, L., Hu, D., Huang, Z., Guo, X., Ba, J., Huang, H., Yuan, S., & Zhou, J. (2025). Positioning Error Analysis of Distributed Random Array Based on Unmanned Aerial Vehicles in Collaborative Jamming. Drones, 9(4), 234. https://doi.org/10.3390/drones9040234