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MIMO Radar Accurate 3-D Imaging and Motion Parameter Estimation for Target with Complex Motions

机译:复杂运动目标的MIMO雷达精确3D成像和运动参数估计

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摘要

In this paper, three-dimensional (3-D) multiple-input multiple-output (MIMO) radar accurate localization and imaging method with motion parameter estimation is proposed for targets with complex motions. To characterize the target accurately, a multi-dimensional signal model is established including the parameters on target 3-D position, translation velocity, and rotating angular velocity. For simplicity, the signal model is transformed into three-joint two-dimensional (2-D) parametric models by analyzing the motion characteristics. Then a gridless method based on atomic norm optimization is proposed to improve precision and simultaneously avoid basis mismatch in traditional compressive sensing (CS) techniques. Once the covariance matrix is obtained by solving the corresponding semi-definite program (SDP), estimating signal parameters via rotational invariance techniques (ESPRIT) can be used to estimate the positions, then motion parameters can be obtained by Least Square (LS) method, accordingly. Afterwards, pairing correction is carried out to remove registration errors by setting judgment conditions according to resolution performance analysis, to improve the accuracy. In this way, high-precision imaging can be realized without a spectral search process, and any slight changes of target posture can be detected accurately. Simulation results show that proposed method can realize accurate localization and imaging with motion parameter estimated efficiently.
机译:针对复杂运动目标,提出了一种具有运动参数估计的三维(3-D)多输入多输出(MIMO)雷达精确定位与成像方法。为了准确表征目标,建立了多维信号模型,其中包括目标3-D位置,平移速度和旋转角速度的参数。为简单起见,通过分析运动特性,将信号模型转换为三关节二维(2-D)参数模型。然后,提出了一种基于原子范数优化的无网格方法,以提高精度,同时避免传统压缩感知(CS)技术中的基础不匹配。通过求解相应的半定式程序(SDP)获得协方差矩阵后,可以使用通过旋转不变技术(ESPRIT)估计信号参数来估计位置,然后可以通过最小二乘(LS)方法获得运动参数,相应地。之后,进行配对校正以通过根据分辨率性能分析设置判断条件来消除配准错误,从而提高准确性。以此方式,无需光谱搜索过程就可以实现高精度成像,并且可以精确地检测到目标姿势的任何细微变化。仿真结果表明,该方法可以有效地估计运动参数,实现精确的定位和成像。

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