The prior works on near-field target localization have mostly assumed ideal hardware models and thus suffer from two limitations in practice. First, extremely large-scale arrays (XL-arrays) usually face a variety of hardware impairments (HIs) that may introduce unknown phase and/or amplitude errors. Second, the existing block coordinate descent (BCD)-based methods for joint estimation of the HI indicator, channel gain, angle, and range may induce considerable target localization error when the target is very close to the XL-array. To address these issues, we propose in this paper a new three-phase HI-aware near-field localization method, by efficiently detecting faulty antennas and estimating the positions of targets. Specifically, we first determine faulty antennas by using compressed sensing (CS) methods and improve detection accuracy based on coarse target localization. Then, a dedicated phase calibration method is designed to correct phase errors induced by detected faulty antennas. Subsequently, an efficient near-field localization method is devised to accurately estimate the positions of targets based on the full XL-array with phase calibration. Additionally, we resort to the misspecified Cramer-Rao bound (MCRB) to quantify the performance loss caused by HIs. Last, numerical results demonstrate that our proposed method significantly reduces the localization errors as compared to various benchmark schemes, especially for the case with a short target range and/or a high fault probability.

翻译：先前关于近场目标定位的研究大多基于理想硬件模型，因此在实践中存在两个局限性。首先，超大规模阵列（XL-array）通常面临多种硬件损伤，这些损伤可能引入未知的相位和/或幅度误差。其次，现有的基于块坐标下降法的联合估计方法（用于估计硬件损伤指示器、信道增益、角度和距离）在目标非常接近XL-阵列时可能导致显著的目标定位误差。为解决这些问题，本文提出了一种新的三阶段硬件损伤感知近场定位方法，通过高效检测故障天线并估计目标位置来实现。具体而言，我们首先利用压缩感知方法确定故障天线，并基于粗略目标定位提高检测精度。随后，设计了一种专用的相位校准方法，以校正由检测到的故障天线引起的相位误差。接着，基于经过相位校准的完整XL-阵列，设计了一种高效的近场定位方法，以精确估计目标位置。此外，我们借助误设定克拉美-罗界来量化由硬件损伤引起的性能损失。最后，数值结果表明，与多种基准方案相比，我们提出的方法显著降低了定位误差，特别是在目标距离较短和/或故障概率较高的情况下。