Low-complexity GLRT for FDA radar without training data

Abstract 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.