Abstract
The problem of detecting and locating multiple scatterers in multibaseline Synthetic Aperture Radar (SAR) tomography, starting from compressive measurements and applying support detection techniques, is addressed. Different approaches based on the detection of the support set of the unknown sparse vector, that is, of the position of the nonzero elements in the unknown sparse vector, are analyzed. Support detection techniques have already proved to allow a reduction in the number of measurements required for obtaining a reliable solution. In this paper, a support detection method, based on a Generalized Likelihood Ratio Test (Sup-GLRT), is proposed and compared with the SequOMP method, in terms of probability of detection achievable with a given probability of false alarm and for different numbers of measurements.
Highlights
Synthetic Aperture Radar (SAR) tomography exploits a stack of complex-valued SAR images, acquired with different view angle and at different times, for providing the fully 3D scene reflectivity profile along azimuth, range, and elevation directions [1]
We compare the detection performance obtained with different signal to noise ratio (SNR) in the range 0– 10 dB, for PFA = 10−3 and for a scatterers separation distance Ds = 2ρs, with ρs the nominal elevation resolution related to the maximum orthogonal baseline extent ST [1]: ρs
We evaluated the detection performance according to definition (4) of probability of detection, PD = 0.5[P(H1/H1) + P(H2/H2)], to fairly compare the two approaches
Summary
Synthetic Aperture Radar (SAR) tomography exploits a stack of complex-valued SAR images, acquired with different view angle and at different times, for providing the fully 3D scene reflectivity profile along azimuth, range, and elevation directions [1]. In [3,4,5] 3D SAR tomographic techniques, capable of achieving an increased elevation resolution, and based on compressive sampling (CS), have been proposed These techniques exploit the sparsity assumption of the ground reflectivity profile in the elevation direction. CS based techniques have been proved to be very effective for reducing the number of SAR images to be acquired and mitigating the effects due to nonuniform baseline spacing [4] They allow attaining superresolution reconstructions along the elevation direction [4, 5]. We are often interested only in the localization of multiple coherent scatterers and not in their intensity This amounts in solving a sort of detection problem, dealing with the identification of only the position of the nonzero elements in the sparse unknown vector, whereas the full reconstruction of the sparse signal is not required. The compressive measurement capability is evaluated by analyzing the detection performance when decreasing the number of measurements
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