结合SBL的双脉冲频控阵雷达离网目标定位方法

目标定位是雷达信号处理中一个具有重要理论意义与实际意义问题。为解决频控阵雷达传统的目标定位算法存在计算量大、目标真实位置偏离空间离散采样网格等问题。本文将频控阵雷达特性与离网稀疏贝叶斯模型结合提出了基于稀疏贝叶斯学习的双脉冲频控阵雷达离网目标定位算法。频控阵雷达发送两个脉冲，其频率偏移量分别为零和非零，然后基于离网稀疏贝叶斯模型估计目标的方位角与斜距。这种方法可以理解为当频控阵雷达以零频偏发射脉冲时，在角度域中检测目标，然后通过适当选择非零频率偏移量在距离域中对目标定位。仿真结果表明，即使在较粗糙的采样网格下，该算法也能保持较高的估计精度，显示了其优于传统算法的优势，证明了该方法的有效性与可靠性。 

Off-Grid Targets Localization algorithm by A Double-Pulse Frequency Diverse Array Radar based on Sparse Bayesian Learning

Targets localization is an issue of theoretical importance and practical significance in radar signal processing. In order to solve the problems of the traditional targets localization algorithm of Frequency Diverse Array (FDA) radar, such as large amount of calculation and the true location of the target deviates from the spatial discretized sampling grid. In this paper, an off-grid targets localization algorithm by a double-pulse Frequency Diverse Array (FDA) radar based on Sparse Bayesian Learning (SBL) is put forward by combining FDA radar characteristics with off-grid Sparse Bayesian model. The FDA radar transmits two pulses with zero and non-zero frequency offsets, respectively, and then estimates the azimuth angle and slant range of targets based on the off-grid sparse Bayesian model. This approach can be interpreted as detecting the targets in angle dimension when the FDA radar emits pulse with zero offset and then localizing them in range dimension by properly choosing the non-zero frequency offset. According to the simulation, the algorithm can maintain high estimation accuracy even under a coarse sampling grid, have shown advantages over conventional ones, which proves the effectiveness and reliability of the method.