An efficient imaging algorithm for GNSS-R bi-static SAR

Global Navigation Satellite System Reflectometry (GNSS-R) based Bi-static Synthetic Aperture Radar (BSAR) is becoming more and more important in remote sensing, given its low power, low mass, low cost, and real-time global coverage capability. Due to its complex configuration, the imaging for GNSS-R BSAR is usually based on the Back-Projection Algorithm (BPA), which is very time consuming. In this paper, an efficient and general imaging algorithm for GNSS-R BSAR is presented. A Two Step Range Cell Migration (TSRCM) correction is firstly applied. The first step roughly compensates the RCM and Doppler phase caused by the motion of the transmitter, which simplifies the SAR data into the quasi-mono-static case. The second step removes the residual RCM caused by the motion of the receiver using the modified frequency scaling algorithm. Then, a cubic phase perturbation operation is introduced to equalize the Doppler frequency modulation rate along the same range cell. Finally, azimuth phase compensation and geometric correction are completed to obtain the focused SAR image. A simulation and experiment are conducted to demonstrate the feasibility of the proposed algorithm, showing that the proposed algorithm is more efficient than the BPA, without causing significant degradation in imaging quality

Signal processing for microwave imaging systems with very sparse array

This dissertation investigates image reconstruction algorithms for near-field, two dimensional (2D) synthetic aperture radar (SAR) using compressed sensing (CS) based methods. In conventional SAR imaging systems, acquiring higher-quality images requires longer measuring time and/or more elements in an antenna array. Millimeter wave imaging systems using evenly-spaced antenna arrays also have spatial resolution constraints due to the large size of the antennas. This dissertation applies the CS principle to a bistatic antenna array that consists of separate transmitter and receiver subarrays very sparsely and non-uniformly distributed on a 2D plane. One pair of transmitter and receiver elements is turned on at a time, and different pairs are turned on in series to achieve synthetic aperture and controlled random measurements. This dissertation contributes to CS-hardware co-design by proposing several signal-processing methods, including monostatic approximation, re-gridding, adaptive interpolation, CS-based reconstruction, and image denoising. The proposed algorithms enable the successful implementation of CS-SAR hardware cameras, improve the resolution and image quality, and reduce hardware cost and experiment time. This dissertation also describes and analyzes the results for each independent method. The algorithms proposed in this dissertation break the limitations of hardware configuration. By using 16 x 16 transmit and receive elements with an average space of 16 mm, the sparse-array camera achieves the image resolution of 2 mm. This is equivalent to six percent of the λ/4 evenly-spaced array. The reconstructed images achieve similar quality as the fully-sampled array with the structure similarity (SSIM) larger than 0.8 and peak signal-to-noise ratio (PSNR) greater than 25 --Abstract, page iv

Innovative SAR & ISAR Signal Processing

This thesis reports on research into the eld of Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) signal processing. The contributions of this thesis may be divided into two following parts: A new bistatic 3D near eld circular SAR imaging algorithm was devel- oped. High resolution radar imaging is typically obtained by combining wide bandwidth signals and synthetic aperture processing. High range resolution is obtained by using modulated signals whereas high cross range resolution is achieved by coherently processing the target echoes at dierent aspect angles of the target. Anyway, theoretical results have shown that when the aspect angle whereby the target is observed is suf- ciently wide, high resolution target images can be obtained by using continuous wave (CW) radars [2], therefore allowing to reduce hardware costs. In a similar way, three dimensional radar imaging can be per- formed by coherently processing the backscattered eld as a function of two rotation angles about two orthogonal axes [3].Three dimensional tar- get radar imaging can be eciently obtained by means of a 3D Fourier Transform, when the far-eld (planar wave) approximation holds. Oth- erwise, the wavefront curvature has to be accounted for. For this reason, a new algorithm based on a near eld spherical wave illumination that takes into account the wavefront curvature by adopting a planar piece- wise approximation was designed. This means that the wavefront is as- sumed to be locally planar around a given point on the target. The oper- ator that the algorithm uses for the focusing procedure is a space variant focusing function which aims at compensating the propagation losses and the wavefront curvature. The algorithm has been developed under the Microwave Electronic Imaging Security and Safety Access (MELISSA) project. The system MELISSA is a body scanner whose purpose is the detection of concealed objects. The added value of the system is the capability to provide an electromagnetic image of the concealed objects. The author would like to thank all people that worked at the project, all LabRass colleagues, all people who designed and acquired real data, all people that permitted the drafting of the rst part of this thesis. The developed algorithm was presented in the chapter 1. The goal of this work was the system design concerning the imaging point of view, by simulating and therefore predicting the system performance by means of the developed algorithm. In the chapter 2 was shown how the design was achieved. Finally, in the chapter 3, the results on real data measured in anechoic chamber with a system with characteristics very close to the nal system prototype MELISSA, was presented. A new way of ISAR processing has been dened, by applying the tradi- tional ISAR processing to data acquired from passive radars. Purpose of the ISAR processing is to extract an electromagnetic bi-dimensional im- age of the target in order to determine the main geometric features of the target, allowing (when possible) recognition and classication. Passive radars are able to detect and track targets by exploiting illuminators of opportunity (IOs). In this work of thesis, it will be proven that the same concept can be extended to allow for Passive Inverse Synthetic Aperture Radar (P-ISAR) imaging. A suitable signal processing is detailed that is able to form P-ISAR images starting from range-Doppler maps, which represent the output of a passive radar signal processing. Multiple chan- nels Digital Video Broadcasting - Terrestrial (DVB-T) signals are used to demonstrate the concept as they provide enough range resolution to form meaningful ISAR images. The problem of grating lobes, generated by DVB-T signal, is also addressed and solved by proposing an innovative P-ISAR technique. The second part of this thesis has been developed un- der the Array Passive ISAR adaptive processing (APIS) project. APIS is dened as a multichannel, bi-static single receiver for array passive radar, capable of detecting targets and generating ISAR images of the detected targets for classication purposes. The author would like to thank all people that worked at the project, all LabRass colleagues, all people who designed, built the prototype and acquired real data, all people that per- mitted the drafting of the second part of this thesis. In the chapter 4, the basics on Passive Bistatic Radar (PBR) was brie y recalled, the P-ISAR processor was detailed and the new algorithm per the Grating Lobes Cancellation was presented. In the chapter 5, some numerical results on simulated data was shown, in order to demonstrate the potentiality of the P-ISAR, for the imaging and classication purpose. In fact, by using more than three adjacent channels and by observing the signal for a long time, ner range and cross-range resolutions, respectively, could be achieved. Finally, the obtained results on real data was discussed in the chapter 6

Mixed Compressive Sensing Back-Projection for SAR Focusing on Geocoded Grid

This article presents a new scheme called 2-D mixed compressive sensing back-projection (CS-BP-2D), for synthetic aperture radar (SAR) imaging on a geocoded grid, in a single measurement vector frame. The back-projection linear operator is derived in matrix form and a patched-based approach is proposed for reducing the dimensions of the dictionary. Spatial compressibility of the radar image is exploited by constructing the sparsity basis using the back-projection focusing framework and fast solving the reconstruction problem through the orthogonal matching pursuit algorithm. An artifact reduction filter inspired by the synthetic point spread function is used in postprocessing. The results are validated for simulated and real-world SAR data. Sentinel-1 C-band raw data in both monostatic and space-borne transmitter/stationary receiver bistatic configurations are tested. We show that CS-BP-2D can focus both monostatic and bistatic SAR images, using fewer measurements than the classical approach, while preserving the amplitude, the phase, and the position of the targets. Furthermore, the SAR image quality is enhanced and also the storage burden is reduced by storing only the recovered complex-valued points and their corresponding locations

Joint shape and motion estimation from echo-based sensor data

2018 Fall.Includes bibliographical references.Given a set of time-series data collected from echo-based ranging sensors, we study the problem of jointly estimating the shape and motion of the target under observation when the sensor positions are also unknown. Using an approach first described by Stuff et al., we model the target as a point configuration in Euclidean space and estimate geometric invariants of the configuration. The geometric invariants allow us to estimate the target shape, from which we can estimate the motion of the target relative to the sensor position. This work will unify the various geometric- invariant based shape and motion estimation literature under a common framework, and extend that framework to include results for passive, bistatic sensor systems

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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