Improved OAM-Based Radar Targets Detection Using Uniform Concentric Circular Arrays

Without any relative moves or beam scanning, the novel Orbital-Angular-Momentum(OAM)-based radar targets detection technique using uniform concentric circular array (UCCA) shows the azimuthal estimation ability, providing new perspective for radar system design. However, the main estimation method Fast Fourier Transform (FFT) under this scheme suffers from low resolution. As a solution, this paper rebuilds the model and introduces the multiple signal classification (MUSIC) algorithm to improve the resolution for detecting targets within the main lobes. The spatial smoothing technique is proposed to tackle the coherent problem brought by the model. Simulation results demonstrate the super-resolution estimation capacity of the MUSIC algorithm for detecting targets within main lobes. In addition, the performance of the MUSIC algorithm to detect targets not illuminated by the main lobes is also evaluated. Despite the MUSIC algorithm loses the resolution advantage under this case, its estimation is more robust than the FFT approach. Overall, the proposed MUSIC method partly improves the performance of the OAM-based radar technique in terms of the high resolution ability, good robustness for detecting targets within and out of the main lobes.