Thank you very much for downloading spotlight synthetic aperture radar signal processing algorithms. Maybe you have knowledge that, people have search numerous times for their favorite books like this spotlight synthetic aperture radar signal processing algorithms, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some harmful virus inside their laptop.

Spotlight Synthetic Aperture Radar Signal Processing Algorithms

: This report discusses the use of modern 2-D spectral estimation algorithms for SAR imaging, and makes two principal contributions to the field of adaptive SAR imaging. First, it is a comprehensive comparison of 2-D spectral estimation methods for SAR imaging. It provides a synopsis of the algorithms available, discusses their relative merits for SAR imaging, and illustrates their performance on simulated and collected SAR imagery. The discussion of autoregressive linear predictive techniques (ARLP), including the Tufts Kumaresan variant, is somewhat more general than appears in most of the literature, in that it allows the prediction element to be varied throughout the subaperture. This generality leads to a theoretical link between ARLP and one of Pisarenko's methods. The report also provides a theoretical analysis that predicts the impact of the adaptive sidelobe reduction (ASR) algorithm on target to clutter ratio and provides insight into order and constraint selection. Second, this work develops multi-channel variants of three related algorithms, minimum variance method (MVM), reduced rank MVM (RRMVM), and ASR to estimate both reflectivity intensity and interferometric height from polarimetric displaced-aperture interferometric data. Examples illustrate that MVM and ASR both offer significant advantages over Fourier methods for estimating both scattering intensity and interferometric height, and allow empirical comparison of the accuracies of Fourier, MVM, and- ometric height estimates.

SAR Imaging via Modern 2-D Spectral Estimation Methods. Volume 1. Imaging Methods.

Passive Radar signifying the localisation of a target by radar measurements without using own controlled emissions has been discussed, tried, reinvented, and matured within the last 80 years. Its advantages, like covert operation and saving the costs of a transmitter, are obvious. Military as well as civilian interests combined with the advances in technological developments have recently boosted research on passive radar and passive radar systems are currently approaching the market. This tutorial shall give an overview of the history, development, and processing in passive radar and enable the interested reader to further investigate the subject exploiting the presented material together with the cited references.

Tutorial: Passive radar tutorial

As recently demonstrated, passive radars are able to detect and track targets by exploiting illuminators of opportunity. In this paper, it will be proven that the same concept can be extended to passive inverse synthetic aperture radar (P-ISAR) imaging. A suitable type of signal processing is proposed that is able to form P-ISAR images starting from range-Doppler maps, which represent the output of passive-radar signal processing. Multiple-channel digital television broadcasting (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, which are generated by the DVB-T signal, is also addressed and solved.

Passive ISAR With DVB-T Signals

Sparsity and Shannon entropy have been widely used in inverse synthetic aperture radar (ISAR) imaging. The minimum entropy criterion is usually applied in the translational motion compensation and the sparse constraint is used in the azimuth imaging. In this paper, we combine these two criteria to develop a novel autofocusing algorithm for sparse aperture ISAR (SA-ISAR) imaging. First, the Laplace approximation-based variational Bayesian inference with the Laplacian scale mixture prior is proposed for SA-ISAR imaging. Then, the autofocusing is accomplished by minimizing the image entropy of the radar image reconstructed within the sparse Bayesian framework. During the iterations, the sparse constraint and the minimum entropy criteria are interactively used to improve the convergence speed and the robustness to noise. Additionally, the proposed autofocusing algorithm is adaptive and does not need to specifically initialize the phase error with other autofocusing approaches. Experimental results, based on both simulated and measured data, validate that the proposed algorithm can even obtain a well-focused image with SA data containing only 12.5% randomly sampled pulses.

Autofocusing for Sparse Aperture ISAR Imaging Based on Joint Constraint of Sparsity and Minimum Entropy

Non-cooperative moving targets typically appear defocused in synthetic aperture radar (SAR) images as they are non-stationary during the coherent processing interval. Typically, the problem of refocusing moving targets in SAR scenes is addressed as a problem of the target's motion estimation and compensation. In this study, the problem is addressed as a problem of non-cooperative target imaging and the authors propose a solution based on the use of inverse synthetic aperture radar (ISAR) processing to solve this problem. Taking into consideration the advantages of SAR processing, the problem is tackled starting from formed SAR images, where non-cooperative targets images are firstly detected, then backprojected to the received data domain and reprocessed as ISAR data. The effectiveness of the proposed method is then tested on Cosmo-Skymed spotlight SAR data of maritime targets.

ISAR based techniques for refocusing non-cooperative targets in SAR images

Inverse synthetic aperture radar (ISAR) images can be obtained using digital video broadcasting–terrestrial (DVB-T)–based passive radars. However, television broadcast–transmitted signals offer poor range resolution for imaging purposes, because they have a narrower bandwidth with respect to those transmitted by a dedicated ISAR system. To reach finer range resolutions, signals composed of multiple DVB-T channels are required. Problems arise, however, because DVB-T channels are typically widely separated in the frequency domain. The gaps between channels produce high grating lobes in the image domain when Fourier-based algorithms are used to form the ISAR image. In this paper, compressive sensing theory is investigated to address this problem because of its ability to reconstruct sparse signals by using incomplete measures. By solving an optimization problem under the constraint of signal sparsity, passive ISAR images can be obtained with strongly reduced grating lobes. Both simulation and experimental results are shown to demonstrate the validity of the proposed approach.

Compressive sensing–based algorithm for passive bistatic ISAR with DVB-T signals

The applicability of compressive sensing (CS) to inverse synthetic aperture radar (ISAR) imagery has been widely discussed over the past few years. In particular, CS-based ISAR image-reconstruction algorithms have been developed and their effectiveness proven when dealing with incomplete ISAR data. Resolution enhancement has also been identified as a case for which CS can be effectively applied to ISAR imagery. In this case, the acquired signal can be interpreted as incomplete data in the frequency/slow-time domain and CS used to reconstruct the super-resolved ISAR image. In this paper, an exhaustive performance analysis is carried out along with a comparison between CS and conventional super-resolution techniques. Several concepts and methods have been introduced in order to effectively define the performance, which is not simply based on visual inspection.

ISAR Image Resolution Enhancement: Compressive Sensing Versus State-of-the-Art Super-Resolution Techniques

The applicability of compressive sensing (CS) to radar imaging has been recently proven and its capability to construct reliable radar images from a limited set of measurements demonstrated. In this study, a common framework for inverse synthetic aperture radar (ISAR) imaging via CS is provided and a CS-based ISAR imaging method is proposed. The proposed method is tested for application such as image reconstruction from compressed data, resolution enhancement and image reconstruction from gapped data. The effectiveness of the proposed method is demonstrated on real datasets and the performance evaluated by means of image contrast.

Compressive sensing-based inverse synthetic radar imaging imaging from incomplete data

Moving target often appear defocussed in SAR images due to their relative motion with respect to the SAR center scene. In several applications that require target classification and/or recognition an unfocussed image of a target would likely lead to misclassification, therefore decreasing classification performances. In this paper, the authors present a new method for refocussing moving targets starting from formed Single Look Complex (SLC) SAR images. Results obtained by applying the proposed techique to Cosmo Skymed (CSK) SLC SAR images show its effectiveness.

Alessio Bacci

Papers

ISAR based techniques for refocusing non-cooperative targets in SAR images

Compressive sensing–based algorithm for passive bistatic ISAR with DVB-T signals

ISAR Image Resolution Enhancement: Compressive Sensing Versus State-of-the-Art Super-Resolution Techniques

Compressive sensing-based inverse synthetic radar imaging imaging from incomplete data

Refocussing of moving targets in SAR images based on inversion mapping and ISAR processing