Time-Frequency Reversion-Based Spectrum Analysis Method and Its Applications in Radar Imaging

Spectrum analysis (SA) plays an important role in radar signal processing, especially in radar imaging algorithm design. Because it is usually hard to obtain the analytical expression of spectrum by the Fourier integral directly, principle of stationary phase (POSP)-based SA is applied to approximat...

Full description

Bibliographic Details
Main Authors: Jixiang Fu, Mengdao Xing, Guangcai Sun
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/13/4/600
id doaj-d358d2954c2043de8a0c6516be5fc30d
record_format Article
spelling doaj-d358d2954c2043de8a0c6516be5fc30d2021-02-09T00:01:16ZengMDPI AGRemote Sensing2072-42922021-02-011360060010.3390/rs13040600Time-Frequency Reversion-Based Spectrum Analysis Method and Its Applications in Radar ImagingJixiang Fu0Mengdao Xing1Guangcai Sun2National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaSpectrum analysis (SA) plays an important role in radar signal processing, especially in radar imaging algorithm design. Because it is usually hard to obtain the analytical expression of spectrum by the Fourier integral directly, principle of stationary phase (POSP)-based SA is applied to approximate this integral. However, POSP requires the phase of the signal to vary rapidly, which is not the case in circular synthetic aperture radar (SAR) and turntable inverse SAR (ISAR). To solve this problem, a new SA method based on time-frequency reversion (TFRSA) is proposed, which utilizes the relationship of the Fourier transform pairs and their corresponding signal phases. In addition, the connection between the imaging geometry and time-frequency relationship is also analyzed and utilized to help solve the time-frequency reversion. When the TFRSA is applied to the linear trajectory SAR, the obtained spectrum expression is the same as the result of POSP. When it is applied to ISAR, the spectrum expressions of near-field and far-field are derived and their difference is found to be position-independent. Based on this finding, an extended polar format algorithm (EPFA) for near-field ISAR imaging is proposed, which can solve the distortion and defocusing problems caused by traditional ISAR imaging algorithms. When it is applied to the circular SAR (CSAR), a new and efficient imaging method based on EPFA is proposed, which can solve the low efficiency problem of conventional BP-based CSAR imaging algorithms. The simulations and real data processing results are provided to validate the effectiveness of proposed method.https://www.mdpi.com/2072-4292/13/4/600spectrum analysis (SA)time-frequency reversion (TFR)radar imagingsynthetic aperture radar (SAR)near-field inverse SAR (ISAR)circular SAR (CSAR)
collection DOAJ
language English
format Article
sources DOAJ
author Jixiang Fu
Mengdao Xing
Guangcai Sun
spellingShingle Jixiang Fu
Mengdao Xing
Guangcai Sun
Time-Frequency Reversion-Based Spectrum Analysis Method and Its Applications in Radar Imaging
Remote Sensing
spectrum analysis (SA)
time-frequency reversion (TFR)
radar imaging
synthetic aperture radar (SAR)
near-field inverse SAR (ISAR)
circular SAR (CSAR)
author_facet Jixiang Fu
Mengdao Xing
Guangcai Sun
author_sort Jixiang Fu
title Time-Frequency Reversion-Based Spectrum Analysis Method and Its Applications in Radar Imaging
title_short Time-Frequency Reversion-Based Spectrum Analysis Method and Its Applications in Radar Imaging
title_full Time-Frequency Reversion-Based Spectrum Analysis Method and Its Applications in Radar Imaging
title_fullStr Time-Frequency Reversion-Based Spectrum Analysis Method and Its Applications in Radar Imaging
title_full_unstemmed Time-Frequency Reversion-Based Spectrum Analysis Method and Its Applications in Radar Imaging
title_sort time-frequency reversion-based spectrum analysis method and its applications in radar imaging
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2021-02-01
description Spectrum analysis (SA) plays an important role in radar signal processing, especially in radar imaging algorithm design. Because it is usually hard to obtain the analytical expression of spectrum by the Fourier integral directly, principle of stationary phase (POSP)-based SA is applied to approximate this integral. However, POSP requires the phase of the signal to vary rapidly, which is not the case in circular synthetic aperture radar (SAR) and turntable inverse SAR (ISAR). To solve this problem, a new SA method based on time-frequency reversion (TFRSA) is proposed, which utilizes the relationship of the Fourier transform pairs and their corresponding signal phases. In addition, the connection between the imaging geometry and time-frequency relationship is also analyzed and utilized to help solve the time-frequency reversion. When the TFRSA is applied to the linear trajectory SAR, the obtained spectrum expression is the same as the result of POSP. When it is applied to ISAR, the spectrum expressions of near-field and far-field are derived and their difference is found to be position-independent. Based on this finding, an extended polar format algorithm (EPFA) for near-field ISAR imaging is proposed, which can solve the distortion and defocusing problems caused by traditional ISAR imaging algorithms. When it is applied to the circular SAR (CSAR), a new and efficient imaging method based on EPFA is proposed, which can solve the low efficiency problem of conventional BP-based CSAR imaging algorithms. The simulations and real data processing results are provided to validate the effectiveness of proposed method.
topic spectrum analysis (SA)
time-frequency reversion (TFR)
radar imaging
synthetic aperture radar (SAR)
near-field inverse SAR (ISAR)
circular SAR (CSAR)
url https://www.mdpi.com/2072-4292/13/4/600
work_keys_str_mv AT jixiangfu timefrequencyreversionbasedspectrumanalysismethodanditsapplicationsinradarimaging
AT mengdaoxing timefrequencyreversionbasedspectrumanalysismethodanditsapplicationsinradarimaging
AT guangcaisun timefrequencyreversionbasedspectrumanalysismethodanditsapplicationsinradarimaging
_version_ 1724278921193586688