Use of Genetic Algorithms for Contrast and Entropy Optimization in ISAR Autofocusing

Image contrast maximization and entropy minimization are two commonly used techniques for ISAR image autofocusing. When the signal phase history due to the target radial motion has to be approximated with high order polynomial models, classic optimization techniques fail when attempting to either maximize the image contrast or minimize the image entropy. In this paper a solution of this problem is proposed by using genetic algorithms. The performances of the new algorithms that make use of genetic algorithms overcome the problem with previous implementations based on deterministic approaches. Tests on real data of airplanes and ships confirm the insight

Fast and Accurate ISAR Focusing Based on a Doppler Parameter Estimation Algorithm

This letter deals with inverse synthetic aperture radar (ISAR) autofocusing of noncooperative moving targets. The relative motion between the target and the sensor, which provides the angular diversity necessary for ISAR imagery, is also responsible for unwanted range migration and phase changes generating defocusing. In the case of noncooperative targets, the relative motion is unknown: the ISAR needs, hence, to implement an autofocus step [motion compensation (MoCo)] to achieve high resolution imaging. This task is typically carried out via the optimization of functionals based on general image quality parameters. In this letter, we propose the use of a fast and accurate MoCo algorithm based on the estimation of the Doppler parameters, thus fully coping with the nature of the imaging system. The effectiveness of the proposed method is proven on both simulated data and data acquired by operational systems

Three-dimensional ISAR imaging: a review

Three-dimensional (3D) inverse synthetic aperture radar (ISAR) imaging has been proven feasible by combining traditional ISAR imaging and interferometry. Such technique, namely inteferometric ISAR (In-ISAR), allows for the main target scattering centres to be mapped into a 3D spatial domain as point clouds. Specifically, the use of an In-ISAR system can overcome the main geometrical interpretation issues imposed by the monostatic acquisition geometry as the problem of cross-range scaling and unknown image projection plane (IPP). However, some issues remain such as scatterer scintillation, shadowing effects, poor SNR etc., which limit the effectiveness of 3D imaging. A solution to such unsolved issues can be found in the use of multiple 3D views, which can be obtained exploiting either multi-temporal or multi-perspective configurations or a combination of both. This study aims to review the main concepts to produce multi-view 3D ISAR images by using In-ISAR systems also presenting real data collected with a multi-static In-ISAR system

Proportional similarity-based Openmax classifier for open set recognition in SAR images

Most of the existing Non-Cooperative Target Recognition (NCTR) systems follow the “closed world” assumption, i.e., they only work with what was previously observed. Nevertheless, the real world is relatively “open” in the sense that the knowledge of the environment is incomplete. Therefore, unknown targets can feed the recognition system at any time while it is operational. Addressing this issue, the Openmax classifier has been recently proposed in the optical domain to make convolutional neural networks (CNN) able to reject unknown targets. There are some fundamental limitations in the Openmax classifier that can end up with two potential errors: (1) rejecting a known target and (2) classifying an unknown target. In this paper, we propose a new classifier to increase the robustness and accuracy. The proposed classifier, which is inspired by the limitations of the Openmax classifier, is based on proportional similarity between the test image and different training classes. We evaluate our method by radar images of man-made targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Moreover, a more in-depth discussion on the Openmax hyper-parameters and a detailed description of the Openmax functioning are given

Virtual multichannel SAR for ground moving target imaging

Slow moving ground targets are invisible within synthetic aperture radar (SAR) images since they appear defocused and their backscattered signal completely overlap the focused ground return. In order for this targets to be detected and refocused the availability of some spatial degrees of freedom is required. This allows for space/slow time processing to be applied to mitigate the ground clutter. However, multichannel SAR (M-SAR) systems are very expensive and the requirements in terms of baseline length can be very restrictive. In this study a processing scheme that exploits high PRF single channel SAR system to emulate a multichannel SAR is presented. The signal model for both target and clutter components are presented and the difference with respect to an actual M-SAR are highlighted. The effectiveness of the proposed processing is then demonstrated on simulated a measured dataset

Compressive sensing for interferometric inverse synthetic aperture radar applications

The applicability of interferometric inverse synthetic aperture radar (InISAR) techniques to images reconstructed via compressive sensing (CS)-based algorithms is investigated. Specifically, the three-dimensional (3D) reconstruction algorithm is applied after exploiting CS for data compression and image reconstruction. The InISAR signal model is derived and formalised in a CS framework. A comparison between conventional CS reconstruction and global sparsity constrained reconstruction techniques is performed for different compression rates and different signal-to-noise ratio conditions. Performances on the 2D and 3D reconstructions are evaluated. Results obtained on real data acquired during the NATO-SET 196 trial are shown