Super-resolving multiple scatterers detection in SAR Tomography assisted by correlation information

TL;DR

This paper introduces a two-step SAR tomography method that accurately detects multiple closely spaced scatterers with lower computational cost and better performance at low SNR compared to compressed sensing approaches.

Contribution

A novel two-step detection method for SAR tomography that improves accuracy and reduces computational complexity in resolving multiple scatterers.

Findings

Outperforms CS-based methods in accuracy and speed

Effective in low SNR conditions

Demonstrated superior 3D reconstruction in simulations

Abstract

This paper proposes a method for detecting multiple scatterers (targets) in the elevation direction for synthetic aperture radar (SAR) tomography. The proposed method can resolve closely spaced targets through a twostep procedure. In the first step, coarse detection is performed with a successive cancellation scheme in which possible locations of targets are marked. Then, in the second step, by searching in the reduced search space which is finely 10 gridded, the accurate location of the targets is found. For estimating the actual number of targets, a model order selection scheme is used in two cases of known and unknown noise variance. Also, by analytical investigation of the probability of detection for the proposed method, the effect of the influential parameters on the detection ability is explicitly demonstrated. Compared to the super-resolution methods based on compressed sensing (CS), the proposed method has a lower computational cost and higher estimation accuracy, especially at low signal-to-noise ratio regime. 15 Simulation results show the superiority of the proposed method in terms of both 3D scatterer reconstruction and detection ability