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.

Radar Echo Parameter Estimation Using Sparse Time-Frequency Analysis Method

2014 ◽  
Vol 543-547 ◽  
pp. 2229-2233
Author(s):  
Hao Chen ◽  
Jun Hai Guo
Keyword(s):  
Parameter Estimation ◽  
Frequency Analysis ◽  
Computational Cost ◽  
Velocity Estimation ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Adaptive Data Analysis

The echoes of pulse radar from maneuvering targets are amplitude modulation and frequency modulation (AM-FM) signal. At present, the methods of estimating parameters of AM-FM signal are time-frequency analysis method, empirical mode decomposition and empirical wavelet transform based adaptive data analysis methods. This paper takes the idea of intrinsic mode function in guessing the initial phase, and applies the newly developed sparse time-frequency analysis method in AM-FM signal parameter estimation. Simulation results show that the estimating performance of this method in AM-FM signal is good under different SNR and it has low computational cost, and this method is applicable in target acceleration and velocity estimation.

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.

The Certification for Inevitability of Cross-Term in WVD

2014 ◽  
Vol 1030-1032 ◽  
pp. 1930-1933 ◽  
Author(s):  
Zhong Hu Yuan ◽  
Man Yang Xu ◽  
Xiao Xuan Qi
Keyword(s):  
Mathematical Method ◽  
Frequency Analysis ◽  
Cross Term ◽  
Time Frequency Analysis ◽  
Important Method ◽  
Time Frequency ◽  
Multicomponent Signal ◽  
Mathematical Properties ◽  
The Cross

WVD has become an important method for time-frequency analysis because of its mathematical properties. In fact, however, cross-term disturbs its application seriously when the multicomponent signal existed. Now increasing people is going to find pure mathematical method to deal with the cross-term in WVD. But this paper will prove the inevitability of cross-term in WVD.

scholarly journals Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4102
Author(s):  
Tomas A. Garcia-Calva ◽  
Daniel Morinigo-Sotelo ◽  
Oscar Duque-Perez ◽  
Arturo Garcia-Perez ◽  
Rene de J. Romero-Troncoso
Keyword(s):  
Fault Detection ◽  
Frequency Analysis ◽  
Induction Motors ◽  
Spectral Estimation ◽  
Diagnostic Techniques ◽  
Frequency Resolution ◽  
Minimum Norm ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Stationary Conditions

In this work, a new time-frequency tool based on minimum-norm spectral estimation is introduced for multiple fault detection in induction motors. Several diagnostic techniques are available to identify certain faults in induction machines; however, they generally give acceptable results only for machines operating under stationary conditions. Induction motors rarely operate under stationary conditions as they are constantly affected by load oscillations, speed waves, unbalanced voltages, and other external conditions. To overcome this issue, different time-frequency analysis techniques have been proposed for fault detection in induction motors under non-stationary regimes. However, most of them have low-resolution, low-accuracy or both. The proposed method employs the minimum-norm spectral estimation to provide high frequency resolution and accuracy in the time-frequency domain. This technique exploits the advantages of non-stationary conditions, where mechanical and electrical stresses in the machine are higher than in stationary conditions, improving the detectability of fault components. Numerical simulation and experimental results are provided to validate the effectiveness of the method in starting current analysis of induction motors.

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