基于广域部署智能反射面的无人机集群跟踪方法

doi:10.12382/bgxb.2022.0217

摘要/Abstract

摘要：

无人机在民用和军事领域中都发挥着重要作用,其体积小、数量多、速度快,更会给国防安全带来严重的安全威胁,有效跟踪定位无人机是保障低空安全的关键问题之一。针对典型城市环境中的多目标跟踪问题,提出一种高效费比的多目标跟踪算法。通过广域部署低成本智能反射面,对多目标进行数据融合;同时提出一种改进的数据关联算法,通过特征辅助的模糊数据关联,利用一部分历史数据作为筛选最优观测数据的特征阈值,得到最接近真实值的量测数据。采用卡尔曼滤波进行状态估计,实现对多目标的低成本高精度跟踪。仿真对比新算法与传统概率密度数据关联算法性能。仿真结果表明:新算法相比传统算法在位置和速度方面均方根误差更小,跟踪精度约为1.7m,传统算法约为6.6m,实验结果表明新算法能够有效提高目标关联精度和跟踪性能。

关键词: 无人机, 多目标跟踪, 广域部署, 智能反射面, 数据关联, 模糊聚类

Abstract:

Unmanned Aerial Vehicles (UAVs) play an important role in both civil and military domains. However, their small size, large quantity, and high speed pose significant security threats to national defense. Ensuring low-altitude safety requires effective tracking and locating of UAVs. A cost-effective target tracking method is thus proposed for tracking multiple targets. By deploying low-cost intelligent reflectors across a wide area, data fusion of multiple targets is performed. An improved data association method is proposed. Through feature-assisted fuzzy data association, a part of historical data is used as the feature threshold to screen the optimal observation data, and the measured data that is closest to the real value is obtained. Finally, Kalman filter is used for state estimation to realize the tracking of multiple targets with low cost and high precision. The performance of the proposed method is compared with that of the traditional probability density data association algorithm. The results show that the proposed algorithm achieves smaller root mean square error in position and speed, with a tracking accuracy of around 1.7m, while the traditional algorithm is about 6.6m. Experimental results verify that the proposed method can effectively improve the target association accuracy and tracking performance.

Key words: unmanned aerial vehicle, multi-target tracking, wide-area deployment, intelligent reflecting surface, data association, fuzzy clustering

郑磊, 陈志敏, 贾宇轩. 基于广域部署智能反射面的无人机集群跟踪方法[J]. 兵工学报, 2023, 44(6): 1837-1845.

ZHENG Lei, CHEN Zhimin, JIA Yuxuan. UAV Swarm Tracking Method Based on Wide-Area Deployment of Intelligent Reflecting Surfaces[J]. Acta Armamentarii, 2023, 44(6): 1837-1845.

图/表 9

图1 多目标跟踪场景

Fig.1 Multi-target tracking scenario

图2 IRS阵列响应

Fig.2 IRS array response

图3 IRS平面示意图

Fig.3 IRS plane diagram

表1 多目标运动参数

Table 1 Multi-target motion parameters

目标	x/m	y/m	v_x/(m·s^-1)	v_y/(m·s^-1)
1	3800	2800	15	-8
2	3800	2500	15	8

图4 目标真实轨迹

Fig.4 Real trajectory of the target

图5 目标位置误差

Fig.5 Target position errors

图6 目标速度误差

Fig.6 Target velocity errors

表2 两种算法均方根误差

Table 2 Root mean square errors of the two algorithms

算法	目标	位置/m		速度/(m·s^-1)
算法	目标	x轴方向	y轴方向	x轴方向	y轴方向
特征辅助 FDA算法	1	0.304	0.486	0.447	1.420
特征辅助 FDA算法	2	0.435	1.978	3.186	0.907
PDA算法	1	8.587	5.106	0.709	3.727
PDA算法	2	6.054	4.173	8.485	1.794

表3 两种算法实验统计结果

Table 3 Statistical results of the two algorithms

算法	跟踪精度/m		关联时间/s
算法	目标1	目标2	关联时间/s
特征辅助的FDA算法	1.698 2	1.747 2	0.038 5
PDA算法	7.198 4	6.041 3	0.007 0

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