In the conventional superresolution, multiple targets are usually separated based on the differences in their locations or Doppler frequencies. The polarization scattering matrix (PSM), as a unique “label” of a target, is very beneficial to radar superresolution, but it is usually neglected. In this paper, a novel scheme of polarimetric superresolution (PSR) is proposed for multitarget detection and localization. We divide the transmit pulse into four parts and modulate the polarization states of each subpulse to derive the sampling in the polarization domain. In the framework of Bayesian compressive sensing (BCS), the estimations of target parameters, including the range, angle and PSM, are derived. However, the off-grid problem results in a number of false targets. To eliminate the false targets, we specially design a true target extractor based on the estimated PSM of each target. With the PSR scheme, each target can be successfully detected and precisely localized in a single pulse. Simulation results demonstrate the effectiveness of the proposed scheme.

中文翻译：

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用于多目标检测和定位的极化超分辨率方案

在传统的超分辨率中，通常根据目标位置或多普勒频率的差异来分离多个目标。极化散射矩阵（PSM）作为目标的唯一“标签”，对雷达超分辨率非常有利，但通常被忽略。本文提出了一种新的极化超分辨率（PSR）方案，用于多目标检测和定位。我们将发射脉冲分为四个部分，并对每个子脉冲的偏振态进行调制，以得出偏振域中的采样。在贝叶斯压缩感知（BCS）框架中，得出目标参数的估计值，包括距离，角度和PSM。但是，离网问题导致许多错误的目标。为了消除错误的目标，我们根据每个目标的估计PSM专门设计了一个真正的目标提取器。使用PSR方​​案，可以成功检测每个目标并将其精确定位在单个脉冲中。仿真结果证明了该方案的有效性。