An Auto-adaptive Accumulation ISAR Imaging Algorithm Based on Time-frequency Analysis

2006 ◽  
Author(s):  
Xiang Jiabin ◽  
Hu Guoqi ◽  
Ma Xiaoyan ◽  
Zhu Yutao
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Imaging Algorithm ◽  
Time Frequency ◽  
Isar Imaging

scholarly journals Application of S-Transform in ISAR Imaging

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 676 ◽  
Author(s):  
Bo Zang ◽  
Mingzhe Zhu ◽  
Xianda Zhou ◽  
Lu Zhong
Keyword(s):  
Frequency Analysis ◽  
Low Frequency ◽  
Frequency Region ◽  
Echo Signal ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Isar Imaging ◽  
Body Components ◽  
Over Time

In inverse synthetic aperture radar (ISAR) imaging, time-frequency analysis is the basic method for processing echo signals, which are reflected by the results of time-frequency analysis as each component changes over time. In the time-frequency map, a target’s rigid body components will appear as a series of single-frequency signals in the low-frequency region, and the micro-Doppler components generated by the target’s moving parts will be distributed in the high-frequency region with obvious frequency modulation. Among various time-frequency analysis methods, S-transform is especially suitable for analyzing these radar echo signals with micro-Doppler (m-D) components because of its multiresolution characteristics. In this paper, S-transform and the corresponding synchrosqueezing method are used to analyze the ISAR echo signal and perform imaging. Synchrosqueezing is a post-processing method for the time-frequency analysis result, which could retain most merits of S-transform while significantly improving the readability of the S-transformation result. The results of various simulations and actual data will show that S-transform is highly matched with the echo signal for ISAR imaging: the better frequency-domain resolution at low frequencies can concentrate the energy of the rigid body components in the low-frequency region, and better time resolution at high frequencies can better describe the transformation of the m-D component over time. The combination with synchrosqueezing also significantly improves the effect of time-frequency analysis and final imaging, and alleviates the shortcomings of the original S-transform. These results will be able to play a role in subsequent work like feature extraction and parameter estimation.

Time-Frequency Analysis for Sar and Isar Imaging

2009 ◽  
pp. 113-127 ◽  
Author(s):  
Igor Djurović ◽  
Ljubiša Stanković ◽  
Vesna Popović ◽  
Miloš Daković ◽  
Thayananthan Thayaparan
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Isar Imaging

scholarly journals An Efficient ISAR Imaging of Targets with Complex Motions Based on a Quasi-Time-Frequency Analysis Bilinear Coherent Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2814
Author(s):  
Cao Zeng ◽  
Mengyi Qin ◽  
Dong Li ◽  
Hongqing Liu ◽  
Yi Chai
Keyword(s):  
Parameter Estimation ◽  
Frequency Analysis ◽  
Frequency Resolution ◽  
Cubic Phase ◽  
Noise Resistance ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Coherent Integration ◽  
Isar Imaging ◽  
The Cross

The inverse synthetic aperture radar (ISAR) imaging for targets with complex motions has always been a challenging task due to the time-varying Doppler parameter, especially at the low signal-to-noise ratio (SNR) condition. In this paper, an efficient ISAR imaging algorithm for maneuvering targets based on a noise-resistance bilinear coherent integration is developed without the parameter estimation. First, the received signals of the ISAR in a range bin are modelled as a multicomponent quadratic frequency-modulated (QFM) signal after the translational motion compensation. Second, a novel quasi-time-frequency representation noise-resistance bilinear Radon-cubic phase function (CPF)-Fourier transform (RCFT) is proposed, which is based on the coherent integration of the energy of auto-terms along the slope line trajectory. In doing so, the RCFT also effectively suppresses the cross-terms and spurious peaks interference at no expense of the time-frequency resolution loss. Third, the cross-range positions of target’s scatters in ISAR image are obtained via a simple maximization projection from the RCFT result to the Doppler centroid axis, and the final high-resolution ISAR image is thus produced by regrouping all the range-Doppler frequency centroids. Compared with the existing time-frequency analysis-based and parameter estimation-based ISAR imaging algorithms, the proposed method presents the following features: (1) Better cross-term interference suppression at no time-frequency resolution loss; (2) computationally efficient without estimating the parameters of each scatters; (3) higher signal processing gain because of 2-D coherent integration realization and its bilinear function feature. The simulation results are provided to demonstrate the performance of the proposed method.

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