摘要
针对低信噪比(SNR)场景下微型无人机探测难题,本文提出了一种基于序贯蒙特卡罗-检测前跟踪(SMC-TBD)的多输入多输出雷达目标跟踪-检测融合方法。区别于跟踪和检测过程相互独立的传统方法,本文方法直接利用三维傅里叶变换后未经阈值处理的雷达原始数据,通过SMC方法计算目标累积存在概率,在实现微型无人机连续检测的同时,完成目标轨迹的高精度跟踪。本文方法的创新在于通过融合检测和跟踪过程,实现了时间-距离-多普勒-方位域目标能量累积,提高了低SNR场景下微型无人机探测性能。实验结果表明,本文方法在SNR低于-20 dB时,微型无人机跟踪性能才逐渐恶化,相比于雷达量测、扩展卡尔曼滤波和粒子滤波提升了约8 dB。
To address the surveillance problem of micro unmanned aerial vehicle(UAV) under low signal-to-noise ratio(SNR) environment, this article proposes an integrated target tracking and detection method based on the sequential Monte Carlo track-before-detect(SMC-TBD) algorithm by utilizing multiple input multiple output radar. Different from conventional methods considering detection and tracking processes independently, the proposed method relies on the raw unthresholding radar data cube after 3 D FFT directly to calculate the accumulative existence probability of the target. In this way, the continuous detection and high precision tracking of micro UAV are achieved simultaneously. The novelty of the proposed method is that it can realize the target energy accumulation of time-range-Doppler-azimuth domain by integrating the detection and tracking process. Therefore, the micro UAV surveillance performance under low SNR condition is improved. Experiment results show that the micro UAV tracking performance of the proposed method deteriorates gradually only when SNR is lower than-20 dB, which can realize 8 dB improvement compared with radar measurements, extended Kalman filter and particle filter.
作者
方鑫
朱婧
黄大荣
张振源
肖国清
Fang Xin;Zhu Jing;Huang Darong;Zhang Zhenyuan;Xiao Guoqing(Mechanical and Electronic Engineering,Southuest Petroleum University,Chengdu610500,China;Institute of Information Science and Engineering,Chongqing Jiaolong Universily,Chongqing 400074China;Chemistry and Chemical Engineering Southuest Petroleum Universily Chengdu610500,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2022年第4期79-88,共10页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(62003064,52174209)
中国博士后科学基金面上项目(2020M683653XB)
四川省科技计划项目(2021YJ0367)
重庆市自然科学基金(cstc2020jcyj-msxmX0797)
重庆市教委科学技术项目(KJQN202000717)
西南石油大学启航计划项目(2021QHZ022)资助。
