采用有限测量的MIMO-FMCW雷达进行多目标微动参数估计
Multi-Target Micro-Motion Parameter Estimation using MIMO-FMCW Radar with Limited Measurements
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中文总结 AI 辅助
本研究提出基于压缩感知的MIMO-FMCW雷达微动参数估计方法，用有限测量实现多目标微动参数估计，可用于无人机探测及运动类型区分。
中文摘要 AI 辅助
本研究提出一种基于压缩感知的方法，用于估计带有旋转部件的目标（如带螺旋桨的小型无人机）的微动参数，所需测量量少于传统方法。采用随机间距稀疏线性天线阵列的多输入多输出（MIMO）调频连续波（FMCW）雷达，发射线性调频（LFM）线性调频信号的随机序列，以在慢时间域实现随机采样。首先估计目标的距离、速度和到达角（AoA）以识别整体运动，然后利用估计参数构建三维点目标响应（3D-PTR），并从总雷达回波中减去该响应，以提取与目标旋转相关的微多普勒特征。这些残余信号在压缩感知（CS）框架内，借助参数化字典，使用一维正交匹配追踪（1D-OMP）算法处理，以联合估计螺旋桨的旋转频率和桨叶长度，该方法还可扩展至多目标场景。仿真结果表明，该方法能在慢时间和空间维度用有限测量准确估计微动参数，验证了其在无人机探测应用中的潜力，随后利用一组决策规则构成的分类框架，将这些估计参数用于区分不同无人机运动类型。
英文摘要
This work presents a compressive sensing-based approach for estimating the micro-motion parameters of targets with rotating components, such as small unmanned aerial vehicles (UAVs) with propellers, using fewer measurements than conventional methods. A multiple-input-multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar employing a randomly spaced sparse linear antenna array is utilized. Random sequences of linear frequency-modulated (LFM) chirps are transmitted to enable random sampling in the slow-time domain. At first, the range, velocity, and angle of arrival (AoA) of the targets are estimated to identify the bulk motion. A three-dimensional point target response (3D-PTR) is then constructed using the estimated parameters and subtracted from the total radar return to extract the micro-Doppler signatures associated with target rotation. These residual signals are processed within a compressive sensing (CS) framework using the one-dimensional orthogonal matching pursuit (1D-OMP) algorithm to jointly estimate the propellers' rotation frequencies and blade lengths with the help of a parametric dictionary. The proposed approach is also extended to a multi-target scenario. Simulation results demonstrate that the proposed approach accurately estimates micro-motion parameters with limited measurements in the slow-time and the spatial dimensions, validating its potential for UAV detection applications. These estimated parameters are then used to distinguish among various UAV motion types using a classification framework based on a set of decision rules.
