主被动协同定位空能资源优化配置方法

吕佩霞; 赵越; 李赞; 白豆; 郝本建

doi:10.19665/j.issn1001-2400.20240102

您当前的位置：

首页 >

文章列表页 >

主被动协同定位空能资源优化配置方法

信息与通信工程 | 更新时间：2024-11-13

- 主被动协同定位空能资源优化配置方法
- Algorithm for optimization of joint spatial and power resources for cooperative active and passive localization
- 西安电子科技大学学报 2024年51卷第4期页码：29-38
- 作者机构：
  
  1. 西安电子科技大学通信工程学院,陕西西安 710071
  2. 西安电子科技大学空天地一体化综合业务网全国重点实验室,陕西西安 710071
- 作者简介：
  
  [ "吕佩霞(1999—),女,西安电子科技大学硕士研究生,E-mail:[email protected]" ]
  [ "赵越(1994—),男,副教授,E-mail:[email protected]" ]
  [ "李赞(1975—),女,教授,E-mail:[email protected]" ]
  [ "白豆(2001—),女,西安电子科技大学硕士研究生,E-mail:[email protected]" ]
  [ "郝本建(1982—),男,教授,E-mail:[email protected]" ]
- 基金信息：
  
  国家重点研发计划(2022YFC3301300);国家自然科学基金(62101403);国家杰出青年科学基金(61825104)
- DOI：10.19665/j.issn1001-2400.20240102
  中图分类号： TN92
- 纸质出版日期：2024-08-20，
  
  网络出版日期：2024-01-24，
  
  收稿日期：2023-07-28，
- 稿件说明：
移动端阅览
吕佩霞, 赵越, 李赞, 等. 主被动协同定位空能资源优化配置方法[J]. 西安电子科技大学学报, 2024,51(4):29-38.

Peixia LYU, Yue ZHAO, Zan LI, et al. Algorithm for optimization of joint spatial and power resources for cooperative active and passive localization. [J]. Journal of Xidian University, 2024,51(4):29-38.
吕佩霞, 赵越, 李赞, 等. 主被动协同定位空能资源优化配置方法[J]. 西安电子科技大学学报, 2024,51(4):29-38. DOI： 10.19665/j.issn1001-2400.20240102.

Peixia LYU, Yue ZHAO, Zan LI, et al. Algorithm for optimization of joint spatial and power resources for cooperative active and passive localization. [J]. Journal of Xidian University, 2024,51(4):29-38. DOI： 10.19665/j.issn1001-2400.20240102.

摘要

无人机的快速发展为当今社会带来巨大便利

但其潜在的滥用现象对公共安全构成严重威胁

因此面向无人机的监测与定位技术近年来得到广泛研究。针对远距低飞无人机难以准确定位的应用问题

提出以无源为主、有源为辅的主被动协同定位框架

在基于到达时间差实现无源被动定位的基础上

引入支持往返到达时间测量的有源主动探测设备

择机对无人机进行主动式定位

补偿无源定位缺失的目标高程信息

从而提升无人机的三维定位精度。为充分挖掘主被动的协同定位潜力

文中深入探究无源被动定位节点预先部署的情况下

有源主动定位节点的空域和能域资源的配置方式

推导了主被动协同定位框架下的定位精度衡量指标

构建了空能资源联合优化问题

提出了基于非线性收敛因子和记忆指导的改进灰狼优化的空能资源优化算法。仿真结果表明

针对无人机定位时

主被动协同定位效果优于无源被动定位

典型场景下高程定位精度显著提升约96.33%。此外

所提的空能资源优化算法在求解空能资源联合优化问题时

性能优于标准(传统)灰狼算法、改进灰狼算法等。

Abstract

The rapid development of UAVs has brought great convenience to today's society

but their potential misuse poses a risk to public safety.As a result

in recent years

surveillance and localization technologies for UAVs have been widely studied.In response to the application problem of difficulty in accurate localization of long-range low-flying UAVs

a cooperative localization framework is proposed

mainly for passive localization

and it is supplemented by active detection.Based on the passive localization using the time difference of arrival(TDOA)

the active detection equipment supporting round-trip time of arrival(RT-TOA) measurement is introduced to locate the UAVs opportunistically and actively.These devices compensate for the missing target elevation information of passive localization

to improve the three-dimensional localization accuracy of UAVs.This paper delves into the spatial and power sources allocation methods for active localization nodes under the pre-deployment of passive localization nodes.Under the framework of cooperative localization

it derives the localization accuracy measurement indicator and formulates the joint optimization problem for spatial and power resources.A resource optimization algorithm for improved gray wolf optimization based on nonlinear convergence factors and memory guidance(CM-IGWO) is proposed.Simulation results show that the active and passive cooperative localization effect is better than the passive localization effect

and that the elevation localization accuracy in typical scenarios is significantly improved by 96.33%.In addition

the proposed CM-IGWO algorithm is superior to the gray wolf optimization(GWO) and IGWO when solving the joint optimization problem for spatial and power resources.

关键词

协同定位到达时间差往返到达时间改进灰狼优化联合优化

Keywords

cooperative localizationtime difference of arrivalround-trip time of arrivalimproved gray wolf optimizationjoint optimization algorithm

references

THIEN H T, PHAM Q V, NGUYEN T V, et al. RF-UAVNet:High-Performance Convolutional Network for RF-Based Drone Surveillance Systems[J]. IEEE Access, 2022,10:49696-49707.

XUE C, LI T, LI Y. Radio Frequency Based Distributed System for Noncooperative UAV Classification and Positioning[J]. Journal of Information and Intelligence, 2023, 2(1):42-51.

BHATTACHERJEE U, OZTURK E, OZDEMIR O, et al. Experimental Study of Outdoor UAV Localization and Tracking Using Passive RF Sensing[C]//Proceedings of the 15th ACM Workshop on Wireless Network Testbeds,Experimental Evaluation & Characterization. New York: ACM,2021:31-38.

LYU P, ZHAO Y, LI Z, et al. On the Performance Analysis of Cooperative Active Round-Trip Ranging and Passive Hyperbolic Localization for UAV Surveillance[C]//2023 IEEE/CIC International Conference on Communications in China(ICCC).Piscataway:IEEE, 2023: 1-6.

LI K, JIAO Y, SONG Y, et al. Passive Localization of Multiple Sources Using Joint RSS and AOA Measurements in Spectrum Sharing System[J]. China Communications, 2021, 18(12):65-80.

Al-SAMAHI S S A, HO K C, ISLAM N E. Improving Elliptic/Hyperbolic Localization Under Multipath Environment Using Neural Network for Outlier Detection[C]//2019 IEEE Conference on Computer Communications Workshops(INFOCOM WKSHPS).Piscataway:IEEE, 2019: 33-38.

SHEN Y, DAI W, WIN M Z. Optimal Power Allocation for Active and Passive Localization[C]//2012 IEEE Global Communications Conference(GLOBECOM).Piscataway:IEEE, 2012: 3713-3718.

LI Z, TIAN Z, NIE W, et al. Configurable Multipath-Assisted Indoor Localization Using Active Relay[J]. IEEE Transactions on Microwave Theory and Techniques, 2022, 70(1):155-168.

MILANI I, BONGIOANNI C, COLON F, et al. Fusing Measurements from Wi-Fi Emission-Based and Passive Radar Sensors for Short-Range Surveillance[J]. Remote Sensing, 2021, 13(18):3556.

ZHANG W, SHI C, SALOUS S, et al. Convex Optimization-Based Power Allocation Strategies for Target Localization in Distributed Hybrid Non-Coherent Active-Passive Radar Networks[J]. IEEE Transactions on Signal Processing, 2022,70:2476-2488.

顾一凡, 赵文龙, 唐善军, 等. 分布式主/被动成像探测系统目标空间协同定位方法研究[J]. 空天防御, 2021, 4(4):119-126.

GU Yifan, ZHAO Wenlong, TANG Shanjun, et al. Research on Target Spatial Collaborative Positioning Methods for Distributed Active/Passive Imaging Detection System[J]. Air & Space Defense, 2021, 4(4):119-126.

AUBRY A, BRACA P, DEMAIO A, et al. 2-D PBR Localization Complying with Constraints Forced by Active Radar Measurements[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(5):2647-2660.

ZHANG T, QIN C, MOLISCH A F, et al. Joint Allocation of Spectral and Power Resources for Non-Cooperative Wireless Localization Networks[J]. IEEE Transactions on Communications, 2016, 64(9):3733-3745.

XIE M, YI W, KIRUBARAJAN T, et al. Joint Node Selection and Power Allocation Strategy for Multitarget Tracking in Decentralized Radar Networks[J]. IEEE Transactions on Signal Processing, 2018, 66(3):729-743.

HUANG L, ZHAO Y, LI Z, et al. Exploiting Spectral Resource Allocation Scheme for TDOA-Based Multiple Source Localization[C]//2022 14th International Conference on Wireless Communications and Signal Processing(WCSP).Piscataway:IEEE, 2022: 489-494.

SHI W, LI J, CHENG N, et al. Multi-Drone 3-D Trajectory Planning and Scheduling in Drone-Assisted Radio Access Networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(8):8145-8158.

赵越, 李赞, 李冰, 等. 面向多源时差定位的鲁棒节点部署算法[J]. 西安电子科技大学学报, 2022, 49(6):15-22.

ZHAO Yue, LI Zan, LI Bing, et al. Robust Node Placement in TDOA-Based Multiple Sources Localization[J]. Journal of Xidian University, 2022, 49(6):15-22.

SHEN Y, WIN M Z. Fundamental Limits of Wideband Localization— Part I:A General Framework[J]. IEEE Transactions on Information Theory, 2010, 56(10):4956-4980.

ZHAO Y, LI Z, CHENG N, et al. Joint UAV Position and Power Optimization for Accurate Regional Localization in Space-Air Integrated Localization Network[J]. IEEE Internet of Things Journal, 2021, 8(6):4841-4854.

YANG J, LIANG T, ZHANG T. Deployment Optimization in UAV Aided Vehicle Localization[C]//2021 IEEE 93rd Vehicular Technology Conference(VTC2021-Spring).Piscataway:IEEE, 2021:1-6.

姜奇, 赵晓敏, 赵贵川, 等. 自适应分数级融合的多模态生物特征认证[J]. 西安电子科技大学学报, 2023, 50(4):11-21.

JIANG Qi, ZHAO Xiaomin, ZHAO Guichuan, et al. Adaptive Score-Level Fusion for Multi-Modal Biometric Authentication[J]. Journal of Xidian University, 2023, 50(4):11-21.

MIRJALILI S. How Effective Is the Grey Wolf Optimizer in Training Multi-Layer Perceptrons[J]. Applied Intelligence, 2015, 43(1):150-161.

NADIMI-SHAHRAKI M H, TAGHIAN S, MIRJALILI S. An Improved Grey Wolf Optimizer for Solving Engineering Problems[J]. Expert Systems with Applications, 2021,166:113917.

张良, 郑丽冬, 冷祥彪, 等. 基于灰狼算法的风-光-抽水蓄能联合系统多目标优化策略研究(2023)[J/OL].[2024-01-03].https://doi.org/10.16183/j.cnki.jsjtu.2023.049https://dx.doi.org/10.16183/j.cnki.jsjtu.2023.049.

LONG W, JIAO J, LIANG X, et al. Inspired Grey Wolf Optimizer for Solving Large-Scale Function Optimization Problems[J]. Applied Mathematical Modelling, 2018,60:112-126.

YAO X, LIU Y. Evolutionary Programming Made Faster[J]. IEEE Transactions on Evolutionary Computation, 1999, 3(2):82-102.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

利用高斯牛顿迭代的时频差无源定位算法

能时频域信息融合的信源被动定位

非视距环境下RSS和TDOA联合的信源被动定位

一种减小NLOS影响的TDOA／AOA数据融合定位算法

直接时差计数超宽带标签定位系统