A novel TomoSAR imaging method with few observations based on nested array

TL;DR

This paper introduces a nested array-based TomoSAR imaging method that enhances degrees of freedom and image quality with fewer observations, using a novel sparse reconstruction algorithm for improved performance.

Contribution

The paper proposes a nested array configuration for TomoSAR and a new sparse reconstruction algorithm that together improve imaging with fewer observations and better noise robustness.

Findings

Enhanced imaging quality compared to traditional methods

Improved anti-noise performance in simulated and real data

Increased degrees of freedom and virtual aperture

Abstract

Synthetic aperture radar tomography (TomoSAR) baseline optimization technique is capable of reducing system complexity and improving the temporal coherence of data, which has become an important research in the field of TomoSAR. In this paper, we propose a nested TomoSAR technique, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co-array through nested array to increase the degrees of freedom (DoF) of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co-array, covariance matrix of the echo needs to be obtained. Therefore, we propose a TomoSAR sparse reconstruction algorithm based on nested array, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. We demonstrate the effectiveness of the proposed method through simulated and real data experiments. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of our proposed method have a better anti-noise performance and retain more image information.