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Range Doppler ISAR Imaging Using Chirp Pulse
Published in Anupama Namburu, Soubhagya Sankar Barpanda, Recent Advances in Computer Based Systems, Processes and Applications, 2020
G. V. Sai Swetha, P. Anjali Reddy, A. Naga Jyothi
Inverse Synthetic Aperture Radar (ISAR) is a dominant technique in signal processsing, which provides the electromagnetic image of the target in two dimensions. This radar imaging technique is used in any weather condition. The technique ISAR has been derived from the Synthetic Aperture Radar (SAR) technique. The radar is kept stationary as the target moves round as shown in figure 1. In SAR it increases the size of antenna so as to increase the resolution of the image. As the radar moves, at many positions pulses are being transmitted, and the returned signals (echoes) are increased in antenna aperture [1]. Likewise, the ISAR is employed to produce moving target’s images in Matlab simulation; the rest of the process is similar to the SAR. The techniques SAR and ISAR are being the highly interested platforms for the researches.
High-Resolution Step-Frequency Radar
Published in James D. Taylor, Ultra-wideband Radar Technology, 2018
Resolution in the cross range is obtained by resolving the Doppler frequency shift caused by the relative motion between the radar and the target in the azimuth (or cross-range) dimension. Different scatterers on target in the cross-range dimension have different line of sight velocities toward the radar and would give different Doppler frequency shifts. Thus, resolving the frequency shift will resolve the scatterers in the cross range. In synthetic aperture radar (SAR) imaging, it is the radar platform that provides relative motion whereas, in inverse synthetic aperture radar (ISAR), it is the target that provides the relative motion. However, in either case, relative motion should involve aspect angle changes between the radar and the target. Cross-range resolution is inversely proportional to the aspect angle change. Obviously, rotating the target will provide aspect angle change. Generally, translational motion between radar and target have some aspect angle change too, unless there is an unlikely situation in which their velocity vectors are pointed toward one another. In this section, discussion will be from the ISAR point of view, although the general principles are the same for both SAR and ISAR.
Applications of Windows
Published in K. M. M. Prabhu, Window Functions and Their Applications in Signal Processing, 2018
The basic principle of ISAR imaging is to coherently collect the scattered large bandwidth echoes produced due to the rotation of the object, which brings about a change in the viewing angle to the radar. By processing the echo signals collected, information of the individual point scatterers on the target object and their relative range can be derived. Therefore, the radar image can be assumed to consist of many energy points called scattering centers. ISAR signal processing consists of the following steps [18]: Range compression deconvolves the echoed signal from the transmitted signal, thereby forming the range profile.Motion compensation registers the moving targets with respect to the radar.Next, the image is constructed by arranging the received signal samples in a polar grid of different viewing angles and Doppler frequencies in frequency spatial domain.
A moving ISAR-object recognition using pi-sigma neural networks based on histogram of oriented gradient of edge
Published in International Journal of Image and Data Fusion, 2022
Asma Elyounsi, Hatem Tlijani, M.S. Bouhlel
The proposed approach was designed to automatically classify the radar target and obtain the highest recognition rate, the data having a specific number of characteristics according to each edge of target. The datasets were taken into account at the University of Brest. The approach has been implemented using MATLAB R2009b on a system equipped with an Intel® Core ™ i3, a 4GB 2.53 GHz Ram and a Microsoft Windows7 operating system. And a comparative analysis was made between HOGE-SPNN, HOG-SPNN and Edge-SPNN. The datasets were prepared by dividing them into 3 folders corresponding to our three targets and each folder is also divided into two folders each one contain 50% of the total images of the targets i (i = 1 to 3) and they are used equally for training and testing process (50% of images for training and the rest is used for testing). We use a 2D/3D Inverse Synthetic data simulated in the following various condition; we illuminated each target by a Frequency Stepped Signal (FFS) with a bandwidth B and frequency increment Δf. The ISAR image represents spatial distribution of scattering centres and produced by azimuth (cross-range) and range analysis. To this fact, the Inverse Fast Fourier Transform (IFFT) is used to construct ISAR images. The parameters used in acquisition and reconstruction of ISAR images are summarised in Table 2