A synthetic aperture radar system operating in congested frequency bands suffers from radio frequency inter ference (RFI) from narrowband sources. When RFI interference is suppressed by frequency notching, gaps are introduced into the fast time phase history. This results in a missing data spectral estimation problem, where the missing data increases sidelobe energy and degrades image quality.
The adaptive spectral estimation method Iterative Adaptive Approach (IAA) has been shown to provide higher resolution and lower sidelobes than comparable methods, but at the cost of higher computationally complexity. Current fast IAA algorithms reduce the computational complexity using Toeplitz /Vandermonde structures, but are not applicable for missing data cases because these structures are lost. When the number of missing data samples is small, which often is the case in SAR with RFI, we use a low rank completion to restore the Toeplitz/ Vandermonde structures. We show that the computational complexity of the proposed algorithm is considerably lower than the state-of-the-art and demonstrate the utility on a simulated frequency notched SAR imaging problem.
Researchers have recently proposed a widely separated multiple-input multiple-output (MIMO) radar using
monopulse angle estimation techniques for target tracking. The widely separated antennas provide improved
tracking performance by mitigating complex target radar cross-section fades and angle scintillation. An adaptive
array is necessary in this paradigm because the direct path from any transmitter could act as a jammer at
a receiver. When the target-free covariance matrix is not available, it is critical to include robustness into
the adaptive beamformer weights. This work explores methods of robust adaptive monopulse beamforming
techniques for MIMO tracking radar.
We investigate the usage of an adaptive method, the Iterative Adaptive Approach (IAA), in combination with
a maximum a posteriori (MAP) estimate to reconstruct high resolution SAR images that are both sparse and
accurate. IAA is a nonparametric weighted least squares algorithm that is robust and user parameter-free. IAA
has been shown to reconstruct SAR images with excellent side lobes suppression and high resolution enhancement.
We first reconstruct the SAR images using IAA, and then we enforce sparsity by using MAP with a sparsity
inducing prior. By coupling these two methods, we can produce a sparse and accurate high resolution image
that are conducive for feature extractions and target classification applications. In addition, we show how IAA
can be made computationally efficient without sacrificing accuracies, a desirable property for SAR applications
where the size of the problems is quite large. We demonstrate the success of our approach using the Air Force
Research Lab's "Gotcha Volumetric SAR Data Set Version 1.0" challenge dataset. Via the widely used FFT,
individual vehicles contained in the scene are barely recognizable due to the poor resolution and high side lobe
nature of FFT. However with our approach clear edges, boundaries, and textures of the vehicles are obtained.