Experimental Synthetic Aperture Radar with Dynamic Metasurfaces

We investigate the use of a dynamic metasurface as the transmitting antenna for a synthetic aperture radar (SAR) imaging system. The dynamic metasurface consists of a one-dimensional microstrip waveguide with complementary electric resonator (cELC) elements patterned into the upper conductor. Integrated into each of the cELCs are two diodes that can be used to shift each cELC resonance out of band with an applied voltage. The aperture is designed to operate at K band frequencies (17.5 to 20.3 GHz), with a bandwidth of 2.8 GHz. We experimentally demonstrate imaging with a fabricated metasurface aperture using existing SAR modalities, showing image quality comparable to traditional antennas. The agility of this aperture allows it to operate in spotlight and stripmap SAR modes, as well as in a third modality inspired by computational imaging strategies. We describe its operation in detail, demonstrate high-quality imaging in both 2D and 3D, and examine various trade-offs governing the integration of dynamic metasurfaces in future SAR imaging platforms

An Efficient Polyphase Filter Based Resampling Method for Unifying the PRFs in SAR Data

Variable and higher pulse repetition frequencies (PRFs) are increasingly being used to meet the stricter requirements and complexities of current airborne and spaceborne synthetic aperture radar (SAR) systems associated with higher resolution and wider area products. POLYPHASE, the proposed resampling scheme, downsamples and unifies variable PRFs within a single look complex (SLC) SAR acquisition and across a repeat pass sequence of acquisitions down to an effective lower PRF. A sparsity condition of the received SAR data ensures that the uniformly resampled data approximates the spectral properties of a decimated densely sampled version of the received SAR data. While experiments conducted with both synthetically generated and real airborne SAR data show that POLYPHASE retains comparable performance to the state-of-the-art BLUI scheme in image quality, a polyphase filter-based implementation of POLYPHASE offers significant computational savings for arbitrary (not necessarily periodic) input PRF variations, thus allowing fully on-board, in-place, and real-time implementation

Advanced Synthetic Aperture Radar Based on Digital Beamforming and Waveform Diversity

This paper introduces innovative SAR system concepts for the acquisition of high resolution radar images with wide swath coverage from spaceborne platforms. The new concepts rely on the combination of advanced multi-channel SAR front-end architectures with novel operational modes. The architectures differ regarding their implementation complexity and it is shown that even a low number of channels is already well suited to significantly improve the imaging performance and to overcome fundamental limitations inherent to classical SAR systems. The more advanced concepts employ a multidimensional encoding of the transmitted waveforms to further improve the performance and to enable a new class of hybrid SAR imaging modes that are well suited to satisfy hitherto incompatible user requirements for frequent monitoring and detailed mapping. Implementation specific issues will be discussed and examples demonstrate the potential of the new techniques for different remote sensing applications

Real-time programmable acoustooptic synthetic aperture radar processor

The acoustooptic time-and-space integrating approach to real-time synthetic aperture radar (SAR) processing is reviewed, and novel hybrid optical/electronic techniques, which generalize the basic architecture, are described. The generalized architecture is programmable and has the ability to compensate continuously for range migration changes in the parameters of the radar/target geometry and anomalous platform motion. The new architecture is applicable to the spotlight mode of SAR, particularly for applications in which real-time onboard processing is required

Efficient Raw Signal Generation Based on Equivalent Scatterer and Subaperture Processing for SAR with Arbitrary Motion

An efficient SAR raw signal generation method based on equivalent scatterer and subaperture processing is proposed in this paper. It considers the radar’s motion track, which can obtain the precise raw signal for the real SAR. First, the imaging geometry with arbitrary motion is established, and then the scene is divided into several equidistant rings. Based on the equivalent scatterer model, the approximate expression of the SAR system transfer function is derived, thus each pulse’s raw signal can be generated by the convolution of the transmitted signal and system transfer function, performed by the fast Fourier transform (FFT). To further improve the simulation efficiency, the subaperture and polar subscene processing is used. The system transfer function of pluses for the same subaperture is calculated simultaneously by the weighted sum of all subscenes’ equivalent backscattering coefficient in the same equidistant ring, performed by the nonuniform FFT (NUFFT). The method only involves the FFT, NUFFT and complex multiplication operations, which means the easier implementation and higher efficiency. Simulation results are given to prove the validity of this method

FMCW rail-mounted SAR: Porting spotlight SAR imaging from MATLAB to FPGA

In this work, a low-cost laptop-based radar platform derived from the MIT open courseware has been implemented. It can perform ranging, Doppler measurement and SAR imaging using MATLAB as the processor. In this work, porting the signal processing algorithms onto a FPGA platform will be addressed as well as differences between results obtained using MATLAB and those obtained using the FPGA platform. The target FPGA platforms were a Virtex6 DSP kit and Spartan3A starter kit, the latter was also low-cost to further reduce the cost for students to access radar technology

A sparsity-driven approach for joint SAR imaging and phase error correction

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR

Region-enhanced passive radar imaging

The authors adapt and apply a recently-developed region-enhanced synthetic aperture radar (SAR) image reconstruction technique to the problem of passive radar imaging. One goal in passive radar imaging is to form images of aircraft using signals transmitted by commercial radio and television stations that are reflected from the objects of interest. This involves reconstructing an image from sparse samples of its Fourier transform. Owing to the sparse nature of the aperture, a conventional image formation approach based on direct Fourier transformation results in quite dramatic artefacts in the image, as compared with the case of active SAR imaging. The regionenhanced image formation method considered is based on an explicit mathematical model of the observation process; hence, information about the nature of the aperture is explicitly taken into account in image formation. Furthermore, this framework allows the incorporation of prior information or constraints about the scene being imaged, which makes it possible to compensate for the limitations of the sparse apertures involved in passive radar imaging. As a result, conventional imaging artefacts, such as sidelobes, can be alleviated. Experimental results using data based on electromagnetic simulations demonstrate that this is a promising strategy for passive radar imaging, exhibiting significant suppression of artefacts, preservation of imaged object features, and robustness to measurement noise