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基于传感器选择的无人机群偏差减少定位

Bias-Reduced Localization for Drone Swarm Based on Sensor Selection.

作者信息

Wu Bo, Shen Bazhong, Zhang Yonggan, Yang Li, Wang Zhiguo

机构信息

School of Telecommunications Engineering, Xidian University, Xi'an 710126, China.

Shaanxi Sunny Science and Technology Co., Ltd., Xi'an 710075, China.

出版信息

Sensors (Basel). 2025 Jun 28;25(13):4034. doi: 10.3390/s25134034.

DOI:10.3390/s25134034
PMID:40648290
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12252034/
Abstract

To address the problem of accurate localization of high-speed drone swarm intrusions, this paper adopts time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements, aiming to improve the performance of estimating the motion state of drone swarms. To this end, a two-step strategy is proposed in this study. Firstly, a small number of sensor nodes with random locations are selected in the wireless sensor network, and the constraint-weighted least squares (CWLS) method is used to obtain the rough position and speed information of the drone swarm. Based on this rough information, the objective function of node optimization is constructed and solved using the randomized semidefinite program (SDP) algorithm proposed in this paper to screen out the sensor nodes with optimal localization performance. Secondly, the sensor nodes screened in the first step are used to re-localize the drone swarm, and the CWLS problem is constructed by combining the TDOA and FDOA measurements, and a deviation elimination scheme is proposed to further improve the localization accuracy of the drone swarm. Simulation results show that the randomized SDP algorithm proposed in this paper has the optimal localization effect, and moreover, the bias reduction scheme proposed in this paper can make the localization error of the drone swarm reach the Cramér-Rao Lower Bound (CRLB) with a low signal-to-noise ratio (SNR).

摘要

为了解决高速无人机群入侵的精确定位问题，本文采用到达时间差（TDOA）和到达频率差（FDOA）测量方法，旨在提高无人机群运动状态估计的性能。为此，本研究提出了一种两步策略。首先，在无线传感器网络中随机选择少量传感器节点，采用约束加权最小二乘法（CWLS）获取无人机群的粗略位置和速度信息。基于此粗略信息，构建节点优化目标函数，并使用本文提出的随机半定规划（SDP）算法求解，以筛选出具有最优定位性能的传感器节点。其次，使用第一步筛选出的传感器节点对无人机群进行重新定位，结合TDOA和FDOA测量构建CWLS问题，并提出一种偏差消除方案以进一步提高无人机群的定位精度。仿真结果表明，本文提出的随机SDP算法具有最优的定位效果，此外，本文提出的偏差减小方案能够使无人机群在低信噪比（SNR）情况下的定位误差达到克拉美罗下界（CRLB）。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b97e/12252034/27447cd8e547/sensors-25-04034-g010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b97e/12252034/710a47498ed8/sensors-25-04034-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b97e/12252034/27447cd8e547/sensors-25-04034-g010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b97e/12252034/e297bb9bf67c/sensors-25-04034-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b97e/12252034/710a47498ed8/sensors-25-04034-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b97e/12252034/27447cd8e547/sensors-25-04034-g010.jpg

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本文引用的文献

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Sensors (Basel). 2025 Jan 28;25(3):780. doi: 10.3390/s25030780.
2
Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review.无人机检测与分类技术的进展与挑战：最新综述
Sensors (Basel). 2023 Dec 26;24(1):125. doi: 10.3390/s24010125.
3
Drone Swarms as Networked Control Systems by Integration of Networking and Computing.群无人机作为网络控制系统的集成联网和计算。
Sensors (Basel). 2021 Apr 9;21(8):2642. doi: 10.3390/s21082642.