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多平台异构信息融合的航空目标跟踪算法

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该文以高空无人机(UAV)飞艇载双光电传感器,无人机载两坐标雷达对航空目标的精确定位跟踪为研究背景,针对参与融合的传感器均无法独立获得目标位置信息导致传统点迹关联、定位方法失效等问题,提出一种基于多平台异构信息融合的航空目标跟踪算法.首先,在坐标系转换的基础上提出基于角度-距离两级点迹关联算法,从而实现多传感器量测关联.其次,提出基于线面交汇融合定位算法,通过最小二乘法、交汇点投影、距离最近点解算及同源数据压缩确定目标的航迹起始位置.在此基础上,利用空基多平台侦察的异构信息,结合传统无迹卡尔曼滤波器(UKF)设计扩维UKF对航空目标进行跟踪.仿真结果表明,该算法实现了对航空高速目标的高精度跟踪.
Airborne Target Tracking Algorithm Using Multi-Platform Heterogeneous Information Fusion
An innovative aviation target tracking algorithm is presented in this paper,utilizing high-altitude unmanned airship dual photoelectric sensors in conjunction with Unmanned Aerial Vehicle(UAV)-borne two-coordinate radar.The algorithm addresses the challenge of integrating sensor data to accurately track targets when individual sensors lack complete target position information,thus overcoming limitations of traditional point-trace association methods.Initially,a two-level point-trace correlation algorithm based on angle and distance is introduced for multi-sensor measurement association following coordinate system transformation.Subsequently,a line-plane intersection fusion localization algorithm is proposed to determine the initial target track position through techniques such as least squares method,intersection projection,distance nearest point solution,and homologous data compression.Leveraging heterogeneous information from space-based multi-platform reconnaissance,an extended Unscented Kalman Filter(UKF)is designed to track aviation targets by enhancing the traditional UKF.Simulation results demonstrate that this algorithm achieves superior precision in tracking high-speed aerial targets.

Information fusionTarget trackingMeasurement associationExtended Unscented Kalman Filter(UKF)Space-based multi-platform

彭锐晖、郭玮、孙殿星、谭硕、窦钥聪

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哈尔滨工程大学青岛创新发展基地 青岛 266000

海军航空大学信息融合研究所 烟台 264001

中国航天科技创新研究院 北京 100032

信息融合 目标跟踪 量测关联 扩维无迹卡尔曼滤波器 空基多平台

2024

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子:1.302
ISSN:1009-5896
年,卷(期):2024.46(9)