摘要
自适应压缩感知与处理方法(Adaptive Compressive Sensing and Processing,ACSP)能够减少计算负荷,但现有的基于自适应压缩感知与处理的雷达目标跟踪方法仅限于单目标的跟踪,针对该问题,提出将自适应压缩感知用于雷达多目标追踪。通过对回波进行稀疏表示,设计改进字典(稀疏变换矩阵)。在测量过程中,采用自适应权重替代随机高斯矩阵,构造和配置感知矩阵,基于压缩感知采样的接收数据来建立测量模型。由于测量与目标状态的非线性关系,采用结合联合概率数据关联方法的似然粒子滤波器对目标状态实时顺序估计,从而克服了多目标跟踪中的数据关联问题。理论仿真实验结果表明,改进的自适应压缩感知与处理方法实现了对多目标跟踪。
Adaptive Compressive Sensing and Processing（ ACSP） can reduce the computational load,but existing radar target tracking methods based on adaptive compressive sensing and processing are limited to single-target tracking. ACSP achieves multi-target tracking. Through the sparse representation of echoes,the improved dictionary（ sparse transformation matrix） is designed. In the measurement process,adaptive weights are used instead of random Gaussian matrices to construct and configure the perceptual matrix.The measurement model is established based on compressed sensing sampled data. This overcomes the data association problem in multi-target tracking. Due to the nonlinear relationship between the measurement and the target state,the likelihood particle filter combined with the joint probability data association method is used to estimate the target state in real time. Theoretical simulation experiments show that the improved adaptive sensing and processing method achieves multi-target tracking.
作者
滕志臣
蒋沅
吴易耘
黄汉江
TENG Zhi-chen;JIANG Yuan;WU Yi-yun;HUANG Han-jiang(School of Information Engineering, Nanehang Hangkong University, Nanehang 330063, Chin)
出处
《南昌航空大学学报（自然科学版）》
CAS
2018年第1期14-22,共9页
Journal of Nanchang Hangkong University(Natural Sciences)
基金
国家自然科学基金(61663030
61663032)
江西省自然科学基金(20142BAB207021)
江西省教育厅科技项目(GJJ150753)
无损检测技术教育部重点实验室开放基金(ZD29529005)
关键词
自适应压缩感知与处理
多目标追踪
感知矩阵
似然粒子滤波
联合概率数据关联
adaptive compression sensing and processing
multi-target tracking
perceived matrix
likelihood particle filtering
joint probability data association
作者简介
[通讯作者]蒋沅（1982-），男，副教授，博士。主要研究方向：先进控制理论及应用、飞行器控制设计、电机控制及优化。
