scholarly journals High Resolution Imaging from Azimuth Missing SAR Raw Data via Segmented Recovery

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 336 ◽  
Author(s):  
Yulei Qian ◽  
Daiyin Zhu
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Greedy Algorithms ◽  
Two Dimensional ◽  
Raw Data ◽  
Reference Function ◽  
Resolution Imaging ◽  
Frequency Aliasing ◽  
Range Migration ◽  
Imaging Algorithms

Synthetic Aperture Radar (SAR) raw data missing occurs when radar is interrupted by various influences. In order to cope with this problem, a new method is proposed to focus the azimuth missing SAR raw data via segmented recovery in this paper. A reference function in time domain is designed to make the missing raw data sparser in two dimensional frequency domain. Afterwards, greedy algorithms are available to recover the missing data in two dimensional frequency domain. In addition, in order to avoid range frequency aliasing problem caused by reference function multiplication in time domain, the missing raw data is split into several parts in range direction and is recovered with a segmented recovery strategy. Then, the recovered raw data is available to be focused with traditional SAR imaging algorithms. The range migration algorithm is chosen to deal with the recovered raw data in this paper. Point target and area target simulations are carried out to validate the effectiveness of the proposed method on azimuth missing SAR raw data. Moreover, the proposed method is implemented on real SAR data in order to further provide convincing demonstration.

scholarly journals Text–Independent Speaker Recognition Using Two–Dimensional Information Entropy

2015 ◽  
Vol 66 (3) ◽  
pp. 169-173 ◽  
Author(s):  
Boško Božilović ◽  
Branislav M. Todorović ◽  
Miroslav Obradović
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Information Entropy ◽  
Speaker Recognition ◽  
Speech Signal ◽  
Experimental Results ◽  
Two Dimensional ◽  
Real Numbers ◽  
The Past ◽  
Signal Features

AbstractSpeaker recognition is the process of automatically recognizing who is speaking on the basis of speaker specific characteristics included in the speech signal. These speaker specific characteristics are called features. Over the past decades, extensive research has been carried out on various possible speech signal features obtained from signal in time or frequency domain. The objective of this paper is to introduce two-dimensional information entropy as a new text-independent speaker recognition feature. Computations are performed in time domain with real numbers exclusively. Experimental results show that the two-dimensional information entropy is a speaker specific characteristic, useful for speaker recognition.

scholarly journals Comparison of Time Domain and Frequency-Wavenumber Domain Ultrasonic Array Imaging Algorithms for Non-Destructive Evaluation

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4951 ◽  
Author(s):  
Zeyu Zhuang ◽  
Jie Zhang ◽  
Guoxuan Lian ◽  
Bruce W. Drinkwater
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Low Noise ◽  
Wavenumber Domain ◽  
Imaging Parameters ◽  
Non Destructive Evaluation ◽  
Ultrasonic Array ◽  
Imaging Algorithms ◽  
Array Imaging ◽  
Non Destructive

Ultrasonic array imaging algorithms have been widely developed and used for non-destructive evaluation (NDE) in the last two decades. In this paper two widely used time domain algorithms are compared with two emerging frequency domain algorithms in terms of imaging performance and computational speed. The time domain algorithms explored here are the total focusing method (TFM) and plane wave imaging (PWI) and the frequency domain algorithms are the wavenumber algorithm and Lu’s frequency-wavenumber domain implementation of PWI. In order to make a fair comparison, each algorithm was first investigated to choose imaging parameters leading to overall good imaging resolution and signal-to-noise-ratio. To reflect the diversity of samples encountered in NDE, the comparison is made using both a low noise material (aluminium) and a high noise material (copper). It is shown that whilst wavenumber and frequency domain PWI imaging algorithms can lead to fast imaging, they require careful selection of imaging parameters.

scholarly journals Efficient Bistatic SAR Raw Signal Simulator of Extended Scenes

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Liang Yang ◽  
Weidong Yu ◽  
Shichao Zheng ◽  
Lei Zhang
Keyword(s):  
Frequency Domain ◽  
Transformation Method ◽  
Geometric Transformation ◽  
Raw Data ◽  
Translation Invariant ◽  
Bistatic Sar ◽  
Range Migration ◽  
The Time Domain ◽  
First Time ◽  
Variant System

Bistatic SAR system is a new mode that allocates the radar transmitter and receiver on different platforms and has more advantages compared to the monostatic case. However, the existing bistatic SAR raw data simulator in the frequency domain can only handle the case of translation invariant system. In this paper, an efficient 2D frequency-domain raw data simulator of extended scenes for bistatic SAR of translation variant system is proposed by a geometric transformation method for the first time, where inverse STOLT interpolation is used to formulate the range migration terms. The presented simulator can accommodate the translation variant bistatic SAR system compared with existing bistatic SAR simulator. And it is more efficient than the time domain one by making use of Fast Fourier Transform (FFT). Simulation results for point targets and a real SAR image demonstrate its validity and effectiveness.

Dimension Reduction Analysis of Signal Manifold for Vowels in Time and Frequency Domain

2021 ◽  
Vol 17 ◽  
pp. 69-74
Keyword(s):  
Dimension Reduction ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Domain Analysis ◽  
High Dimensional ◽  
Dimensional Manifold ◽  
Two Dimensional ◽  
Time Domain Simulation ◽  
Time Frequency ◽  
Reduction Methods

In this paper, the LLE and ISOMAP algorithms in manifold learning are applied them to the analysis of vowel signals in time and frequency domain. Time domain simulation results show that the two dimensionality reduction methods can implement two-dimensional visualization of signals while preserving the high-dimensional manifold structure of original signals as much as possible. The time-frequency domain dimension reduction analysis of vowel signal manifold effectively solves the problem that high-dimensional speech signals can’t be intuitively felt, and provides a new potential way for signal classification. The frequency domain analysis is further optimized on the basis of time domain simulation. Because half of the amplitude values in DFT is used in the simulation, the two-dimensional manifold of the signal is roughly linearly distributed, which can effectively reduce redundancy and make the signal more compactly expressed in the frequency domain

Construction of a two-dimensional-VWOOC based on time domain/frequency domain optical CDMA system

2020 ◽  
Vol 16 (5) ◽  
pp. 384-389
Author(s):  
Zhuo Lu ◽  
Ye Lu ◽  
Chuan-qi Li ◽  
Peng Zhou
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Optical Cdma ◽  
Cdma System ◽  
Two Dimensional

scholarly journals Efficient Simulation of Airborne SAR Raw Data in Case of Motion Errors

2021 ◽  
Author(s):  
Yuhua Guo ◽  
Shichao Jin
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
High Efficiency ◽  
Simulation Approach ◽  
Raw Data ◽  
Scene Simulation ◽  
Efficient Simulation ◽  
Nonlinear Motion ◽  
Airborne Sar ◽  
Computational Resources

In the simulation of SAR raw data, it is well-known that the frequency-domain algorithm is more efficient than a time-domain algorithm, making it is more suitable for extended scene simulation. However, the frequency-domain algorithm is perhaps better suited for ideal linear motion and requires some degrees of approximations to take the nonlinear motion effects. This chapter presents an efficient simulation approach based on hybrid time and frequency-domain algorithms under certain assumptions. The algorithm has high efficiency and is suitable for the simulation of extended scenes, which demands highly computational resources. The computational complexity of the proposed algorithm is analyzed, followed by numerical results to demonstrate the effectiveness and efficiency of the proposed approach.

JOINT INTERPRETATION OF AIRBORNE ELECTROMAGNETIC DATA IN THE TIME DOMAIN AND FREQUENCY DOMAIN

2018 ◽  
Vol 12 (7-8) ◽  
pp. 76-83
Author(s):  
E. V. KARSHAKOV ◽  
J. MOILANEN
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Interval ◽  
Single Spectrum ◽  
Simultaneous Inversion ◽  
Homogeneous Half Space ◽  
Time Domain Data ◽  
The Time Domain

Тhe advantage of combine processing of frequency domain and time domain data provided by the EQUATOR system is discussed. The heliborne complex has a towed transmitter, and, raised above it on the same cable a towed receiver. The excitation signal contains both pulsed and harmonic components. In fact, there are two independent transmitters operate in the system: one of them is a normal pulsed domain transmitter, with a half-sinusoidal pulse and a small "cut" on the falling edge, and the other one is a classical frequency domain transmitter at several specially selected frequencies. The received signal is first processed to a direct Fourier transform with high Q-factor detection at all significant frequencies. After that, in the spectral region, operations of converting the spectra of two sounding signals to a single spectrum of an ideal transmitter are performed. Than we do an inverse Fourier transform and return to the time domain. The detection of spectral components is done at a frequency band of several Hz, the receiver has the ability to perfectly suppress all sorts of extra-band noise. The detection bandwidth is several dozen times less the frequency interval between the harmonics, it turns out thatto achieve the same measurement quality of ground response without using out-of-band suppression you need several dozen times higher moment of airborne transmitting system. The data obtained from the model of a homogeneous half-space, a two-layered model, and a model of a horizontally layered medium is considered. A time-domain data makes it easier to detect a conductor in a relative insulator at greater depths. The data in the frequency domain gives more detailed information about subsurface. These conclusions are illustrated by the example of processing the survey data of the Republic of Rwanda in 2017. The simultaneous inversion of data in frequency domain and time domain can significantly improve the quality of interpretation.

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