Cross-range scaling of inverse synthetic aperture radar images with complex moving targets based on parameter estimation

2017 ◽  
Vol 76 (6) ◽  
pp. 4095-4116
Author(s):  
Guohui Di ◽  
Fulin Su ◽  
Xinbo Xu
Frequenz ◽  
2018 ◽  
Vol 72 (7-8) ◽  
pp. 391-399 ◽  
Author(s):  
Hamid Dehghani ◽  
Navid Daryasafar

Abstract Using Probability Hypothesis Density (PHD) filtering, a novel approach is proposed in this paper for simultaneous tracking of multiple moving targets in received data by Inverse Synthetic Aperture Radar (ISAR) system. Since PHD filtering approach is implemented successively in prediction and update steps, its performance quality will obviously be higher in “Spotlight” imaging mode than in “Stripmap”. Thus, its application to Spotlight mode is generally more logical. The idea to integrate tracking capability into ISAR system processor is to sort radar received data to correct Range Cell Migration (RCM) prior to tracking operations. Clearly, Range Cell Migration Compensation (RCMC) approach is different from this approach in image formation process, in terms of their implementation phase. However, they are implemented in a similar way. As simulation results reveal, applying Range Cell Migration Compensation to the raw data received by ISAR before tracking operation, results in high quality tracking of moving targets.


2021 ◽  
Vol 13 (4) ◽  
pp. 782
Author(s):  
Hongwei Li ◽  
Chao Li ◽  
Shiyou Wu ◽  
Shen Zheng ◽  
Guangyou Fang

Terahertz (THz) imaging technology has received increased attention in recent years and has been widely applied, whereas the three-dimensional (3D) imaging for moving targets remains to be solved. In this paper, an adaptive 3D imaging scheme is proposed based on a single input and multi-output (SIMO) interferometric inverse synthetic aperture radar (InISAR) imaging system to achieve 3D images of moving targets in THz band. With a specially designed SIMO antenna array, the angular information of the targets can be determined using the phase response difference in different receiving channels, which then enables accurate tracking by adaptively adjusting the antenna beam direction. On the basis of stable tracking, the high-resolution imaging can be achieved. A combined motion compensation method is proposed to produce well-focused and coherent inverse synthetic aperture radar (ISAR) images from different channels, based on which the interferometric imaging is performed, thus forming the 3D imaging results. Lastly, proof-of-principle experiments were performed with a 0.2 THz SIMO imaging system, verifying the effectiveness of the proposed scheme. Non-cooperative moving targets were accurately tracked and the 3D images obtained clearly identify the targets. Moreover, the dynamic imaging results of the moving targets were achieved. The promising results demonstrate the superiority of the proposed scheme over the existing THz imaging systems in realizing 3D imaging for moving targets. The proposed scheme shows great potential in detecting and monitoring moving targets with non-cooperative movement, including unmanned military vehicles and space debris.


Author(s):  
Hari Kishan Kondaveeti ◽  
Valli Kumari Vatsavayi

In this chapter, Inverse Synthetic Aperture Radar, a special type of active microwave synthetic aperture radar is introduced and its applications in military surveillance are presented. Then, the basic principles involved in data acquisition and image generation are explained. The issues and challenges involved in processing the ISAR images for autonomous target detection and identification are discussed later. The proposed classification method is explained and its accuracy is evaluated experimentally against the conventional classification method in the rest of the chapter.


2018 ◽  
pp. 2307-2332
Author(s):  
Hari Kishan Kondaveeti ◽  
Valli Kumari Vatsavayi

In this chapter, Inverse Synthetic Aperture Radar, a special type of active microwave synthetic aperture radar is introduced and its applications in military surveillance are presented. Then, the basic principles involved in data acquisition and image generation are explained. The issues and challenges involved in processing the ISAR images for autonomous target detection and identification are discussed later. The proposed classification method is explained and its accuracy is evaluated experimentally against the conventional classification method in the rest of the chapter.


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