  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.
