Conventionally adaptive detection for frequency diverse array (FDA) radar requires extra training data to estimate the range-dependent interference covariance matrix (ICM). In this paper, we propose an adaptive moving target detection approach for FDA radar without training data, where the ICM is directly estimated by applying a structured generalized likelihood ratio test (SGLRT) to the target-present samples. In order to reduce the three-dimensional (3D) search complexity of the SGLRT, we then propose an efficient unstructured GLRT (UGLRT) approach, which transforms the 3D search into a 1D Doppler-only search and a joint range-angle search. Moreover, we design a low-complexity two-stage implementation of the UGLRT for practical application. The first stage is to test multiple discrete frequency bins for target detection, while the second stage is to precisely estimate the frequency through locally optimizing the estimator function around the coarse estimate provided by the first-stage detection. As the UGLRT approach avoids the 3D search, it significantly reduces the computational complexity, compared to the SGLRT counterpart. Numerical results show that the UGLRT approach achieves almost the same detection and estimation performance as the SGLRT one.

传统上，用于频率变化阵列（FDA）雷达的自适应检测需要额外的训练数据来估计与距离相关的干扰协方差矩阵（ICM）。在本文中，我们提出了一种无需训练数据的，适用于FDA雷达的自适应移动目标检测方法，该方法通过将结构化广义似然比测试（SGLRT）应用于目标样本来直接估计ICM。为了降低SGLRT的三维（3D）搜索复杂度，我们提出了一种有效的非结构化GLRT（UGLRT）方法，该方法将3D搜索转换为仅1D多普勒搜索和联合范围角度搜索。此外，我们为实际应用设计了UGLRT的低复杂度两阶段实现。第一步是测试多个离散频点以进行目标检测，而第二阶段则是通过围绕第一阶段检测提供的粗略估计值局部优化估计函数来精确估计频率。由于UGLRT方法避免了3D搜索，因此与SGLRT方法相比，它大大降低了计算复杂度。数值结果表明，UGLRT方法可实现与SGLRT几乎相同的检测和估计性能。