A back-projection autofocus algorithm based on flight trajectory optimization for synthetic aperture radar imaging

Saeedi, Jamal; Faez, Karim

doi:10.1007/s11045-014-0308-1

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…In the following, a frequency domain algorithm for processing raw data will be described. The most frequently used algorithm for SAR signal processing is the range-Doppler algorithm (RDA) [17][18]. It was developed for processing SEASAT SAR data in 1978.…”

Section: Basic Principle Of Sar Signal Modellingmentioning

confidence: 99%

A New Hybrid Method for Synthetic Aperture Radar Deceptive Jamming

Saeedi¹

2019

JMICRO

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Based on the synthetic aperture radar (SAR) geometric model, a novel, and fast algorithm of large scene deceptive jamming against different SAR systems is proposed. First, a template deceptive image is transformed into the time domain signal using inverse image formation algorithm. Then, the transformed signal is convolved with enemy's received SAR signal in order to cope with electronic countercountermeasures (ECCM) techniques. Finally, the generated jamming signal is transmitted to enemy's SAR system for deceptive purpose. Specifically, we have proposed a hybrid method for SAR jamming, which uses a digital radio frequency memory (DRFM) system incorporating with SAR raw data simulation module. The experimental results for the proposed deceptive jammer demonstrate its ability to deceive SAR system.

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Section: Basic Principle Of Sar Signal Modellingmentioning

confidence: 99%

A New Hybrid Method for Synthetic Aperture Radar Deceptive Jamming

Saeedi¹

2019

JMICRO

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“…After navigation‐based MC, autofocus methods are usually applied to enhance image focus by compensating residual phase error [4]. Autofocus can be described as the process of automatic estimation and compensation of residual phase error in SAR images.…”

Section: Introductionmentioning

confidence: 99%

Improved phase curvature autofocus for stripmap synthetic aperture radar imaging

Saeedi

2020

IET signal process.

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Based on the theory of phase curvature autofocus (PCA) on stripmap synthetic aperture radar (SAR), an improved algorithm for increasing the accuracy of phase error compensation is presented in this study. PCA method was proposed to extend the phase gradient autofocus method for SAR systems in stripmap mode. The main problems concerned with the traditional PCA algorithm are related to selecting candidates in the image for phase error estimation, windowing, estimation procedure, and range shift due to the phase error. In this study, the modification of traditional PCA algorithm has been performed in different steps including the following: improving range‐compressed data, prominent points extraction, adaptive windowing, weighted maximum likelihood for phase error estimation, improving phase error result, range shift compensation, and determining the condition to end the iterations. Real data experiments demonstrate the success of the proposed autofocus method, which is applied to the stretched‐based pulsed mode SAR data set in the absence of highly accurate inertial navigation units.

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