Iterative Adaptive Thresholding for 2D Estimation in Sparse Radar Arrays: Performance Analysis and Experimental Validation

We present the application of the iterative method with adaptive thresholding (IMAT) for 2D azimuth-elevation estimation in sparse radar antenna array geometries. While IMAT does not show advantages in angular resolution, it demonstrates superior performance compared to traditional direction-of-arrival (DoA) estimation techniques, such as non-uniform FFT, in reconstructing secondary low radar cross-section (RCS) targets via the enhancement of the measurement dynamic range. In this regard, its target reconstruction capabilities compete with established compressed sensing algorithms like orthogonal matching pursuit (OMP), which in contrast to IMAT are also only applicable to sparse radar scenes. We evaluate the reconstruction performance of this novel approach through simulations with varying levels of array sparsity and validate its principles in measurements using a 150 GHz radar, underscoring its effectiveness in real-world sparse planar array configurations.