A method for the measurement and analysis of SAR signal random phase error

2009 ◽  
Author(s):  
Zhang Peng
Keyword(s):  
Phase Error ◽  
Random Phase ◽  
Measurement And Analysis

scholarly journals High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net

2021 ◽  
Vol 13 (12) ◽  
pp. 2326
Author(s):  
Xiaoyong Li ◽  
Xueru Bai ◽  
Feng Zhou
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Phase Error ◽  
Random Phase ◽  
Back Propagation ◽  
Estimation Method ◽  
Data Driven ◽  
Reconstruction Performance ◽  
Imaging Results ◽  
Isar Imaging

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.

Impact of random phase error on microwave power combining efficiency

2013 ◽  
Vol 25 (11) ◽  
pp. 2914-2918 ◽  
Author(s):  
徐刚 Xu Gang ◽  
徐勇 Xu Yong ◽  
施美友 Shi Meiyou ◽  
余川 Yu Chuan ◽  
廖勇 Liao Yong ◽  
...  
Keyword(s):  
Microwave Power ◽  
Phase Error ◽  
Random Phase ◽  
Power Combining ◽  
Combining Efficiency

Accurate Estimation of Phase Distribution on Reference Plane in Grating Projection Measurement

2005 ◽  
Vol 295-296 ◽  
pp. 221-226 ◽  
Author(s):  
M.R. Zhao ◽  
Yu Cheng Lin ◽  
X.B. Niu ◽  
D.M. Cheng
Keyword(s):  
Phase Distribution ◽  
Phase Error ◽  
Random Phase ◽  
Taylor Series Expansion ◽  
Least Square Method ◽  
Least Square ◽  
Measuring System ◽  
Reference Plane ◽  
Accurate Estimation ◽  
Rational Polynomial

A binary second order rational polynomial is adopted to simulate and extend the phase distribution on reference plane. In order to get the coefficients of the polynomial accurately, iterative least-square method based on the first order Taylor series expansion is used. The effect of the real reference plane profile error on measuring result is reduced by using extended unwrapped phase to substitute the original unwrapped phase. The effects of the random phase error and the system geometrical parameter error are decreased. The measuring accuracy of the system is improved. The principle of the 3D profile measuring system based on grating projection, the theoretic analysis for accurate estimate of phase distribution on reference plane, and the experimental results are presented.

scholarly journals A PVT-Robust Super-Regenerative Receiver with Background Frequency Calibration and Concurrent Quenching Waveform

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1119 ◽  
Author(s):  
Yin ◽  
Fu ◽  
El-Sankary
Keyword(s):  
Data Stream ◽  
Input Data ◽  
Phase Error ◽  
Random Phase ◽  
High Sensitivity ◽  
Cmos Technology ◽  
Low Noise ◽  
Center Frequency ◽  
Noise Performance ◽  
Frequency Calibration

A process-voltage-temperature (PVT)-robust, low power, low noise, and high sensitivity, super-regenerative (SR) receiver is proposed in this paper. To enable high sensitivity and robust-PVT operation, a fast locking phase-locked-loop (PLL) with initial random phase error reduction is proposed to continuously adjust the center frequency deviations of the SR oscillator (SRO) without interrupting the input data stream. Additionally, a concurrent quenching waveform (CQW) technique is devised to improve the SRO sensitivity and its noise performance. The proposed SRO architecture is controlled by two separate biasing branches to extend the sensitivity accumulation (SA) phase and reduce its noise during the SR phase, compared to the conventional optimal quenching waveform (OQW). The proposed SR receiver is implemented at 2.46 GHz center frequency in 180 nm SMIC CMOS technology and achieves better sensitivity, power consumption, noise performance, and PVT immunity compared with existent SR receiver architectures.

scholarly journals Elevation Spatial Variation Error Compensation in Complex Scene and Elevation Inversion by Autofocus Method in GEO SAR

2021 ◽  
Vol 13 (15) ◽  
pp. 2916
Author(s):  
Faguang Chang ◽  
Dexin Li ◽  
Zhen Dong ◽  
Yang Huang ◽  
Zhihua He
Keyword(s):  
Spatial Variation ◽  
Phase Error ◽  
Random Phase ◽  
Synthetic Aperture ◽  
Fast Variable ◽  
Complex Scene ◽  
Phase Gradient ◽  
Disaster Monitoring ◽  
Autofocus Algorithm ◽  
Target Elevation

Due to the high altitude of geosynchronous synthetic aperture radar (GEO SAR), its synthetic aperture time can reach up to several hundred seconds, and its revisit cycle is very short, which makes it of great application worth in the remote sensing field, such as in disaster monitoring and vegetation measurements. However, because of the elevation of the target, elevation spatial variation error is caused in the GEO SAR imaging. In this paper, we focus on the compensation of the elevation space-variant error in the fast variant part with the autofocus method and utilize the error to carry out elevation inversing in complex scenes. For a complex scene, it can be broken down into a slow variant slope and the remaining fast variant part. First, the phase error caused by the elevation spatial variation is analyzed. Second, the spatial variant error caused by the slowly variant slope is compensated with the improved imaging algorithm. The error caused by the remaining fast variable part is the focus of this paper. We propose a block map-drift phase gradient autofocus (block-MD-PGA) algorithm to compensate for the random phase error part. By dividing sub-blocks reasonably, the elevation spatial variant error is compensated for by an autofocus algorithm in each sub-block. Because the errors of different elevations are diverse, the proposed algorithm is suitable for the scene where the target elevations are almost the same after the sub-blocks are divided. Third, the phase error obtained by the autofocus method is used to inverse the target elevation. Finally, simulations with dot-matrix targets and targets based on the high-resolution TerraSAR-X image verify the excellent effect of the proposed method and the accuracy of the elevation inversion.

Properties of Acoustical Speckle in the Presence of Phase Aberration Part I: First Order Statistics

1988 ◽  
Vol 10 (1) ◽  
pp. 12-28 ◽  
Author(s):  
Gregg E. Trahey ◽  
Stephen W. Smith
Keyword(s):  
Tissue Characterization ◽  
Imaging System ◽  
Focal Point ◽  
Phase Error ◽  
Random Phase ◽  
Statistical Properties ◽  
First Order ◽  
Phase Errors ◽  
Speckle Patterns ◽  
Phase Aberrations

The first order statistical properties of acoustical speckle patterns are studied as a function of several types of random and structured phase error. Such errors may arise from tissue velocity inhomogeneities or limitations in the acoustical imaging system. In this paper, we review the theory describing the statistical properties of speckle, describe a computer model which predicts the mean speckle brightness in the presence of phase aberrations, and report experiments in which we measure the effect of these aberrations on speckle brightness and variance. We find that the average speckle brightness is significantly reduced by even mild phase aberrations. The phase aberrations studied include focal point errors, random phase errors, and structured errors. Good agreement is found between experiment and computer simulation. We then discuss the implications of these results for imaging through aberrating media, tissue characterization and phase compensation methods.

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