GNSS Distributed Interference Source Deployment Algorithm Based on Locally Optimal Point

doi:10.12382/bgxb.2023.1022

Abstract

Abstract:

In order to effectively control the satellite navigation signals in a specific area, GNSS(Global Navigation Satellite System)can usually be suppressed by interference sources to implement navigation interference, and the deployment scheme of GNSS interference sources will directly determine whether the ideal interference effect can be achieved. The distributed deployment algorithm of GNSS interference sources is studied based on a single GNSS interference source viewshed analysis method, on the basis of the analysis of the traditional permutation and combination algorithm, a global search algorithm with higher computational efficiency is proposed. For a larger task area is, the concept of locally optimal point is introduced on the basis of the global search algorithm in order to further improve the computational efficiency of the algorithm, and an optimized search algorithm based on locally optimal point is proposed. When th elevel of locally optimal point is 0, the global search algorithm is equivalent to the optimized search algorithm based on locally optimal point, so as to ensure the mathematical unity of the two algorithms. Experimental analysis shows that, compared with the traditional permutation and combination algorithm, the global search algorithm can improve the computational efficiency by more than 99% with little difference in the results when the task area is small. Compared with the global search algorithm, the optimal search algorithm with different levels of locally optimal points can further improve the computational efficiency by 60%~97% when the task area is large.

Key words: satellite navigation, locally optimal point, distributed suppression interference, terrain obstruction, viewshed analysis

CLC Number:

LIU Zhiheng, LIU Weiping, JIAO Bo. GNSS Distributed Interference Source Deployment Algorithm Based on Locally Optimal Point[J]. Acta Armamentarii, 2024, 45(9): 3240-3252.

Figures/Tables 18

References 28

[1]	LU J, GUO X, SU C G. Global capabilities of BeiDou Navigation Satellite System[J]. Satellite Navigation, 2020(1):303-307. DOI:10.1186/s43020-020-00025-9.
[2]	谭述森. 智能化战场的卫星导航[J]. 科学中国人, 2021(33):34-35.
	TAN S S. Satellite navigation for intelligent battlefields[J]. Scie.pngic Chinese, 2021(33):34-35. (in Chinese)
[3]	王月, 郝金明, 刘伟平. 卫星导航欺骗干扰装备效能评估方法[J]. 兵工学报, 2020, 41(1):108-118. doi: 10.3969/j.issn.1000-1093.2020.01.013
	WANG Y, HAO J M, LIU W P. Evaluation method for effectiveness of GNSS spoofer[J]. Acta Armamentarii, 2020, 41(1):108-118. (in Chinese) doi: 10.3969/j.issn.1000-1093.2020.01.013
[4]	焦博, 丛佃伟. 导航干扰技术在无人机防御中的应用展望[J]. 无线电工程, 2021, 51(10):1019-1024.
	JIAO B, CONG D W. Application prospect of navigation jamming in UAV defense[J]. Radio Engineering, 2021, 51(10):1019-1024. (in Chinese)
[5]	SAVAGE O, BABU S J. In a jam[J]. IHS Jane s Defence Weekly, 2023, 60(27):26-29.
[6]	AHMED N, HUANG H. Distributed jammer network: Impact and characterization[C]//Proceedings of the MILCOM 2009-2009 IEEE Military Communications Conference.Boston, MA, US:IEEE, 2009:1-6.
[7]	高军辉. 分布式干扰技术研究[D]. 成都: 电子科技大学, 2006.
	GAO J H. Research on distributed interference technology[D]. Chengdu: University of Electronic Science and Technology of China, 2006. (in Chinese)
[8]	DAI L C, ZHANG H T. Propagation-model-free base station deployment for mobile networks:integrating machine learning and heuristic methods[J]. IEEE Access, 2020, 99:1-1.
[9]	程力睿, 张顺健, 曾芳玲. 卫星导航干扰源优化部署方法[J]. 火力与指挥控制, 2015, 40(7):43-46.
	CHENG L R, ZHANG S J, ZENG F L. Optimal deployment method of satellite navigation interference source[J]. Fire Power and Command Control, 2015, 40(7):43-46. (in Chinese)
[10]	刘帅, 秦姗姗. 基于自适应遗传算法的空管雷达部署优化[J]. 通信技术, 2018, 51(4):870-874.
	LIU S, QIN S S. Optimization of air traffic control radar deployment based on adaptive genetic algorithm[J]. Communication Technology, 2018, 51(4):870-874. (in Chinese)
[11]	付钰, 朱克家, 韩奇, 等. 一种导航信号干扰源部署方法[J]. 导航定位学报, 2020, 8(3):114-118.
	FU Y, ZHU K J, HAN Q, et al. A deployment method of navigation signal interference source[J]. Journal of Navigation and Positioning, 2020, 8(3):114-118. (in Chinese)
[12]	李硕, 徐国伟, 林辉. 基于改进蚁群算法的雷达任务自动部署研究[J]. 国外电子测量技术, 2021, 40(11):41-47.
	LI S, XU G W, LIN H. Research on automatic deployment of radar mission based on improved ant colony algorithm[J]. Foreign Electronic Measurement Technology, 2021, 40(11): 41-47. (in Chinese)
[13]	张鑫鑫. GPSⅢ卫星导航干扰方案和干扰源优化部署方法设计[D]. 成都: 电子科技大学, 2017.
	ZHANG X X. GPSIII design of satellite navigation jamming scheme and optimal deployment method of interference source[D]. Chengdu: University of Electronic Science and Technology of China, 2017. (in Chinese)
[14]	TANG M N, CHEN S J, ZHENG X H, et al. Sensors deployment optimization in multi-dimensional space based on improved particle swarm optimization algorithm[J]. Journal of Systems Engineering and Electronics, 2018, 29(5):969-982. doi: 10.21629/JSEE.2018.05.09
[15]	SRINIVASA R P C, BANKA H, JANA P K. PSO-based multiplesink placement algorithm for protracting the lifetime of wireless sensor networks[J]. Springer India, 2016, 379(1):605-616.
[16]	李奇. 基于分布式算法的雷达组网抗干扰优化部署研究[D]. 西安: 西安电子科技大学, 2023.
	LI Q. Research on anti-interference optimization deployment of radar networking based on distributed algorithm[D]. Xi’an: Xidian University, 2023. (in Chinese)
[17]	RAO D V, RAVISHANKAR M. A methodology for optimal deployment and effectiveness evaluation of air defence resources using game theory[J]. Sādhanā, 2020, 45(1):1-15.
[18]	刘卓承, 张云雷, 刘涛, 等. 基于数据和遗传算法的对海雷达部署优化研究[J]. 指挥控制与仿真, 2023, 45(3):113-118. doi: 10.3969/j.issn.1673-3819.2023.03.017
	LIU Z C, ZHANG Y L, LIU T, et al. Research on deployment optimization of sea radar based on data and genetic algorithm[J]. Command Control and Simulation, 2023, 45(3):113-118. (in Chinese)
[19]	郭宇翔. 基于通信可视域的基站选址方法研究[D]. 南京: 南京师范大学, 2021.
	GUO Y X. Research on base station location selection method based on communication visual domain[D]. Nanjing: Nanjing Normal University, 2021. (in Chinese)
[20]	FISHER P F. First experiments in viewshed uncertainty: simulating fuzzy viewsheds[J]. Photogrammetric Engineering and Remote Sensing, 1992, 58(3):345-352.
[21]	MITCH R H, DOUGHERTY R C, PSIAKI M L, et al. Signal characteristics of civil GPS jammers[C]//Proceedings of the 24th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2011). Portland, OR, US: the Institute of Navigation, 2011:1907-1919.
[22]	SHAHEEN E M, ELGAMEL S A. Mathematical analyses of the GPS receiver interference tolerance and mean time to loss lock[J]. Defence Technology, 2019, 15(3):440-449. doi: 10.1016/j.dt.2019.02.001
[23]	刘丹, 叶小舟, 肖伟, 等. 地形遮挡对GNSS干扰范围影响的高效仿真算法[J]. 系统工程与电子技术, 2020, 42(11):2418-2425. doi: 10.3969/j.issn.1001-506X.2020.11.02
	LIU D, YE X Z, XIAO W, et al. Efficient simulation algorithm for the influence of terrain occlusion on GNSS interference range[J]. Systems Engineering and Electronics, 2020, 42(11): 2418-2425. (in Chinese)
[24]	GUTH P L, NIEKERK A V, GROHMANN C H, et al. Digital elevation models:terminology and definitions[J]. Remote Sensing, 2021, 13(18):3581.
[25]	林强. 基于Cesium的岛屿高精度地理信息系统及关键技术设计[D]. 南京: 南京邮电大学, 2022.
	LIN Q. Design of island high-precision geographic information system based on Cesium and key technologies[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2022. (in Chinese)
[26]	LIN M L, DU C B, CAO X, et al. An improved algorithm for intervisibility judgment based on RSG[C]//Proceedings of the 2018 5th International Conference on Information Science and Control Engineering.Zhengzhou, China:IEEE, 2018:116-120.
[27]	WANG J J, ROBINSON G J, WHITE K. Generating viewsheds without using sightlines[J]. Photogrammetric Engineering and Remote Sensing, 2000, 66(1):87-90.
[28]	FYFE P, KPVACH K. Navstar GPS space segment/navigation user interfaces[J]. Europe Journal of Theology, 1991, 24(5):272.

干扰源部署算法	特点
排列组合算法	部署方案最优,计算效率复杂度较高,实用性较差
全局算法	部署方案较优,计算复杂度较低,计算效率取决于任务区域大小,一般用于较小区域
基于局部最优点的优化搜索算法	部署方案较优,计算复杂度较低,计算效率取决于局部最优点等级,局部最优点等级越高,计算效率越高,适用于任何区域

干扰源部署算法	特点
排列组合算法	部署方案最优,计算效率复杂度较高,实用性较差
全局算法	部署方案较优,计算复杂度较低,计算效率取决于任务区域大小,一般用于较小区域
基于局部最优点的优化搜索算法	部署方案较优,计算复杂度较低,计算效率取决于局部最优点等级,局部最优点等级越高,计算效率越高,适用于任何区域

场景编号	组别序号	接收机抗干扰能力 (干信比)/dB	干扰源发射功率/W	发射天线增益/dB	接收信号功率/dBw	干扰源最大传播距离/m	任务区域大小/m²
1	1-1	100	1	6	-160	30	100×100
	1-2	100	10	2	-160	60	100×100
2	2-1	100	10	2	-160	60	200×200
	2-2	100	9	6	-160	90	200×200

场景编号	组别序号	接收机抗干扰能力 (干信比)/dB	干扰源发射功率/W	发射天线增益/dB	接收信号功率/dBw	干扰源最大传播距离/m	任务区域大小/m²
1	1-1	100	1	6	-160	30	100×100
	1-2	100	10	2	-160	60	100×100
2	2-1	100	10	2	-160	60	200×200
	2-2	100	9	6	-160	90	200×200

场景编号	组别序号	使用干扰源数量/个		计算时间/s		耗时减少	干扰覆盖率/%		结果
场景编号	组别序号	排列组合算法	全局搜索算法	排列组合算法	全局搜索算法	百分比/%	排列组合算法	全局搜索算法	差异/%
1	1-1	5	5	1341.99	0.04	99.99	88.89	83.95	4.94
	1-2	2	2	35.54	0.03	99.92	96.30	93.82	2.48
2	2-1	4	4	16357.95	0.33	99.99	82.35	77.21	5.14
	2-2	3	3	1954.18	0.47	99.98	94.85	85.66	9.19