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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Three-Dimensional High-Resolution MIMO Radar Imaging via OFDM Modulation and Unitary ESPRIT
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Three-Dimensional High-Resolution MIMO Radar Imaging via OFDM Modulation and Unitary ESPRIT

机译:OFDM调制和单一EIMO雷达成像的三维高分辨率MIMO雷达成像

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Imaging and recognition of targets with complex maneuvers bring a new challenge to conventional radar applications. In this paper, the three-dimensional (3D) high-resolution image is attained in real-time by a Multiple-Input-Multiple-Output (MIMO) radar system with single Orthogonal-Frequency-Division-Multiplexing (OFDM) pulse. First, to build the orthogonal transmit waveform set for MIMO transmission, we utilize complex orthogonal designs (CODs) for OFDM subcarrier modulation. Based on the OFDM modulation, a preprocessing method is developed for transmit waveform separation without conventional matched filtering. The result array manifold is the Kronecker product of the steering vectors of subcarrier/transmit antenna/receive antenna uniform linear arrays (ULAs). Then, the high-resolution image of target is attained by the Multidimensional Unitary Estimation of Signal Parameters via Rotational Invariant Techniques (MD-UESPRIT) algorithm. The proposed imaging procedures include the multidimensional spatial smoothing, the unitary transform via backward-forward averaging, and the joint eigenvalue decomposition (JEVD) algorithm for automatically paired coordinates estimation. Simulation tests compare the reconstruction results with the conventional methods and analyze the estimation precision relative to signal-to-noise ratio (SNR), system parameters, and errors.
机译:具有复杂机动的成像和识别目标对传统雷达应用具有新的挑战。在本文中,通过具有单个正交频分复用(OFDM)脉冲的多输入 - 多输出(MIMO)雷达系统实时实现三维(3D)高分辨率图像。首先,为了构建用于MIMO传输的正交发射波形集,我们利用用于OFDM子载波调制的复杂正交设计(COD)。基于OFDM调制,开发了一种用于发送波形分离的预处理方法,而无需常规匹配过滤。结果阵列歧管是子载波/发射天线/接收天线均匀线性阵列(ULAS)的转向矢量的kronecker乘积。然后,通过旋转不变技术(MD-UESPRIT)算法的信号参数的多维酉估计来实现目标的高分辨率图像。所提出的成像过程包括多维空间平滑,通过向后平均的整体变换,以及用于自动配对坐标估计的联合特征值分解(JEVD)算法。仿真测试将重建结果与传统方法进行比较,并分析相对于信噪比(SNR),系统参数和错误的估计精度。

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