A novel imaging algorithm with random phase error mitigation for the FMCW SC-SAR system

2016 ◽  
Author(s):  
Yang Yue ◽  
Cong XunChao ◽  
Long KeYu ◽  
Xie Wei ◽  
Zhang Qing ◽  
...  
Keyword(s):  
Phase Error ◽  
Random Phase ◽  
Imaging Algorithm ◽  
Error Mitigation

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

Amp-Phi: A CSI-Based Indoor Positioning System

2018 ◽  
Vol 32 (09) ◽  
pp. 1858005 ◽  
Author(s):  
Tao-Yun Zhou ◽  
Bao-Wang Lian ◽  
Yi Zhang ◽  
Sen Liu
Keyword(s):  
Nearest Neighbor ◽  
Phase Error ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Statistical Characteristics ◽  
Positioning System ◽  
Indoor Environments ◽  
K Nearest Neighbor ◽  
Error Mitigation ◽  
Indoor Positioning System

With rapid growth in the demand of location-based services (LBS) in indoor environments, localizations based on fingerprinting have attracted significant interest due to their convenience. Until now, most such methods were based on received signal strength indicator (RSSI), which is vulnerable to non-line-of-sight (NLOS). In order to realize high-precision indoor positioning, we propose a channel state information (CSI)-based Amp-Phi indoor-positioning system which exploits the amplitude and phase information of CSI at the same time to establish a fingerprinting database. Firstly, according to the characteristics of the raw CSI information collected at different positions under different environments, we build an NLOS mitigation model and a phase error mitigation model, respectively, to process the amplitude and phase of CSI. Secondly, we analyze the statistical characteristics of CSI carefully, including maximum, minimum, mean and variance. After being processed with the models, the CSI features can be used to distinguish different positions clearly, which provides a theoretical basis for the indoor positioning based on fingerprinting. Finally, we build a fingerprinting database based on the features of amplitude and phase, realize to locate the target’s position with the K-Nearest Neighbor (KNN) matching algorithm. Experiments implemented in different situations show that Amp-Pi system is reliable and robust, whose position accuracy is higher than that of PhaseFi, Horus and machine learning (ML) systems under the same condition. It can be used in many scenarios, such as the localization of robots in our daily life, by doctors or patients in the hospital, for people localization in large supermarkets or museums and so on.

scholarly journals A New Rang-Instantaneous Doppler ISAR Imaging Algorithm

2012 ◽  
Vol 6-7 ◽  
pp. 682-687
Author(s):  
Bao Ping Wang ◽  
Chao Sun ◽  
Jun Jie Guo
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Motion Compensation ◽  
Noise Suppression ◽  
Phase Error ◽  
Doppler Imaging ◽  
Imaging Algorithm ◽  
Time Frequency ◽  
Maneuvering Target ◽  
Isar Imaging

ISAR imaging algorithm based on sparse representation has the advantages of high resolution, noise suppression and dealing with gapped data effectively. The method is based on the hypothesis that the imaging targets move smoothly. But the movement of ISAR imaging targets is usually of high maneuverability, which results in big phase error after motion compensation. Using the traditional RD imaging algorithm and the imaging algorithm based on sparse representation will make the resultant image fuzzy, and can't even be identified. This paper introduces a new range- instantaneous Doppler imaging algorithm based on sparse representation and time-frequency transform, which can effectively image the maneuvering target. The experimental results validate the feasibility of this approach.

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.

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