Elisa Giusti

Target Recognition by Means of Polarimetric ISAR Images

Automatic target recognition (ATR) is generally the reason why inverse synthetic aperture radar (ISAR) imaging systems are employed. Moreover, the use of fully polarimetric radar systems in radar imaging applications such as SAR and ISAR has enhanced both image quality and classification capabilities. The authors propose a novel technique for ATR using polarimetric ISAR (Pol-ISAR) images. The proposed method is based on a model matching approach. Results are obtained that show the effectiveness of such a technique.

Passive ISAR With DVB-T Signals

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.

Automatic Target Recognition by Means of Polarimetric ISAR Images and Neural Networks

Inverse synthetic aperture radar (ISAR) images are often used for classifying and recognizing targets. Moreover, the use of fully polarimetric ISAR (Pol-ISAR) images enhances classification capabilities. In this paper, the authors propose a novel automatic target recognition (ATR) technique based on the use of fully Pol-ISAR images and neural networks (NNs). In order to reduce the amount of data processed by the classifier, the brightest scattering centers are first extracted by means of the Pol-CLEAN technique, and then, their scattering matrices are decomposed using Camerons decomposition. A classifier based on the use of multilayer perceptron NN that makes use of the features extracted from the Pol-ISAR images is then implemented. A proof-of-concept test is performed on real data acquired during a controlled experiment in an anechoic chamber.

Polarimetrically-Persistent-Scatterer-Based Automatic Target Recognition

Reliable automatic target recognition (ATR) systems based on inverse synthetic aperture radar (ISAR) images require a robust feature selection. An ATR system based on polarimetric ISAR images has been recently proposed that extracts bright scatterers and uses their polarimetric signatures to define classification features. Since bright scatterers could be the results of multiple scattering, the concept of polarimetrically persistent scatterers (PPSs) has been introduced in a recent work. PPS is usually associated with single scattering mechanism and, therefore, may prove to be more robust for classification purposes. In this paper, an ATR system is defined that makes use of PPS. Furthermore, a detailed analysis is carried out to emphasize the meaning of PPSs when used for ATR.

Contrast-Optimization-Based Range-Profile Autofocus for Polarimetric Stepped-Frequency Radar

One of the main benefits brought by the use of fully polarimetric radars is the ability to identify scattering mechanisms, which are related to the target physical properties. One of the most critical problems in synthetic range-profile reconstruction is the distortion induced by the target motion. Radial target velocity and acceleration generate second- and third-order phase terms in the received signal, which produce range shift and point-spread-function smearing. The distortions induced by the target motion produce, as a consequence, a signal-to-noise ratio loss. Recently, a method based on contrast maximization has been proposed in order to compensate target radial motions using single-polarization data. In this paper, an extension of such an algorithm is proposed that exploits fully polarimetric data in order to improve the target radial motion compensation.

Range Doppler and Image Autofocusing for FMCW Inverse Synthetic Aperture Radar

Inverse synthetic aperture radar (ISAR) is a well-known technique for obtaining high-resolution radar images. ISAR techniques have been successfully applied in the recent past in combination with pulsed coherent radar. In order to be more appealing to both civilian and military fields, imaging sensors are required to be low cost, low powered, and compact. Coherent pulsed radars do not account for these requirements as much as frequency modulated continuous wave (FMCW) radars. However, FMCW radars transmit a linear frequency modulated (LFM) sweep in a relatively long time interval when compared with the pulse length of a coherent pulse radar. During such an interval the assumption of stop&go is no longer valid, that is the target cannot be considered stationary during the acquisition of the entire sweep echo. Therefore, the target motion within the sweep must be taken into account. Such a problem is formulated and solved for ISAR systems, where the target is noncooperative and additional unknowns are added to the signal model. In the present work, the authors define a complete FMCW-ISAR received signal model, propose an ISAR image formation technique suitable for FMCW radar and derive the point spread function (PSF) of the imaging system. Finally, the proposed FMCW ISAR autofocusing algorithm is tested on simulated and real data.

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

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.

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

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.

Clean technique for polarimetric ISAR

Inverse synthetic aperture radar (ISAR) images are often used for classifying and recognising targets. To reduce the amount of data processed by the classifier, scattering centres are extracted from the ISAR image and used for classifying and recognising targets. This paper addresses the problem of estimating the position and the scattering vector of target scattering centres from polarimetric ISAR images. The proposed technique is obtained by extending the CLEAN technique, which was introduced in radar imaging for extracting scattering centres from single-polarisation ISAR images. The effectiveness of the proposed algorithm, namely, the Polarimetric CLEAN (Pol-CLEAN) is tested on simulated and real data.

Theoretical foundation of passive bistatic ISAR imaging

Passive bistatic inverse synthetic aperture radar (PB-ISAR) has been recently introduced to add an important capability to passive coherent location (PCL) systems. Although evidence of such capability has been provided, a theoretical background that supports such findings is needed to fully comprehend PB-ISAR imaging. This paper provides a full theoretical basis for PB-ISAR including a performance analysis in terms of spatial resolution. Examples with real data are also provided as case studies.