The recursive sidelobe minimization (RSM) algorithm is an iterative method that relies on the incoherent nature of sidelobes to iteratively attenuate them in radar imagery. Grating lobes are points of coherence that arise when a periodic aperture does not satisfy the Nyquist sampling rate. Grating lobes are coherent, so the RSM algorithm cannot iteratively attenuate them in the same manner as sidelobes. Random sampling reduces coherency in resolution cells where targets are not present, and greatly increases the sidelobe energy throughout the imagery. In this paper, random sampling is combined with the RSM algorithm to generate 3-dimensional (3-D) imagery with sparse 2 D apertures. The random aperture sampling avoids the creation of grating lobes, but greatly increases the sidelobe levels throughout the image. Then the RSM algorithm is applied to reduce the sidelobes. This technique is first applied to a simulated point target. Then, it is applied to modeled and experimental data to demonstrate its efficacy with extended targets.
In synthetic aperture radar theory, periodic spatial sampling that satisfies Nyquist theorem can be used to generate imagery with minimal ambiguities. A two-dimensional (2-D) grid of uniformly spaced aperture samples can be used to generate three-dimensional (3-D) radar imagery. However, 2-D apertures typically result in an untenable number of samples for practical implementation. The spacing between aperture samples can be increased to reduce the number of samples at the potential cost of introducing ambiguities. Since the sampling is uniform, this can introduce grating lobes within the image area. Grating lobes are erroneous points of coherence that result from sub sampling (i.e., not satisfying Nyquist theorem) a periodic array. The recursive sidelobe minimization (RSM) algorithm removes sidelobes by exploiting the varying null positions in images formed with random subapertures. However, grating lobe spacing is generally unaffected by subaperture selection in periodic arrays. This paper presents a modification to the RSM algorithm which removes grating lobes by randomizing the operating center frequency for each iteration of the algorithm.
Ultra-wideband (UWB) ground-penetrating radar (GPR) technology has been widely employed for detecting underground targets, structures, or anomalies. However, the backscatter signals from the ground surface pose a critical challenge for downward-looking GPR systems since 1) these ground return signals have significant power compared to the backscatter signal from subsurface targets, and 2) the ground return and target signals completely overlap in both the time and frequency domains. This paper presents a technique for reconstructing and extracting the GRI signals from downward-looking UWB GPR signals. This simultaneous low-rank and sparse algorithm models the GRI signals as a low-rank matrix, while the return signals from the targets are represented by sparse signals. The solver simultaneously optimizes both objectives, resulting in the separation of the target signals from the GRI signals. Our technique performs this GRI extraction directly in the phase history data domain prior to synthetic aperture radar (SAR) image formation. Thus, it can be implemented as an additional step, completely independent from all other steps, in the pre-processing stage. Recovery results from both simulated and real data sets illustrate the robustness and effectiveness of our proposed technique.
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