DOA Signal Identification Based on Amplitude and Phase Estimation for Subarray MIMO Radar Applications

The overlapped equal subarray transmit radar, which is also known as the Subarray Multiple-Input Multiple-Output radar, utilizes the key advantages simultaneously of both types of multi-antenna radar, i.e. the phased array and MIMO radars, so that it is able to detect multiple targets even though it has a radar cross section (RCS) of a weak or small target. In this paper, it is proposed to develop a parameter estimation approach called amplitude and phase estimation (APES). This approach provides improved resolution to the estimation of the amplitude and direction of arrival (DoA) of the target reflection signal on the radar compared to the existing conventional estimation methods such as least squares (LS). The formulation of the APES method on this radar is based on the tested parameters such as DoA and RCS and continuously being evaluated. The results show that the performance of the APES method of this radar can detect targets very precisely when the number of subarrays (M) is greater than the number of detection targets (P), precisely M > P. For the results of DoA and RCS accuracy from the APES method, this radar is more accurate than the LS when testing the angular resolution between the two targets, an angle resolution of 2° is obtained for the APES method which is superior to the LS with an angle resolution of 5.8°. In these conditions, the APES method is able to accurately distinguish between two targets while the LS method is only able to detect one target

Persistent scatterer densification through the application of capon- And APES-Based SAR reprocessing algorithms

Capon's minimum-variance method (MVM) and amplitude and phase estimation (APES) spectral estimation algorithms can be applied to synthetic aperture radar (SAR) processing to improve the resolution and suppress sidelobe levels. In this paper, we use Capon-/APES-based SAR reprocessing algorithms to increase the persistent scatterer (PS) density in PS interferometry (PSI). We propose a PS candidate (PSC) selection algorithm applicable to the superresolution reprocessed images and the corresponding processing chain. The performance of the proposed algorithm is evaluated by a number of simulations and a stack of TerraSAR-X data. The results show that the Capon algorithm outperforms others in PSC selection. We present a full PSI time-series analysis on the PSCs extracted from the Capon-reprocessed stacks. The results show that the PS density is increased between 50% and 60%, while their interferometric quality is maintained.Mathematical Geodesy and Positionin