scholarly journals Spectral-factorisation Root-MUSIC algorithm for super-resolution ISAR imaging

2019 ◽  
Vol 2019 (20) ◽  
pp. 7125-7129
Author(s):  
Qiuchen Liu ◽  
Yong Wang
Keyword(s):  
Super Resolution ◽  
Music Algorithm ◽  
Isar Imaging

scholarly journals Two-Step Root-MUSIC for Direction of Arrival Estimation without EVD/SVD Computation

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Feng-Gang Yan ◽  
Shuai Liu ◽  
Jun Wang ◽  
Ming Jin
Keyword(s):  
Super Resolution ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Low Complexity ◽  
Music Algorithm ◽  
Correlation Matrices ◽  
Noise Subspace ◽  
Using Data ◽  
Eigen Decomposition ◽  
Value Decomposition

Most popular techniques for super-resolution direction of arrival (DOA) estimation rely on an eigen-decomposition (EVD) or a singular value decomposition (SVD) computation to determine the signal/noise subspace, which is computationally expensive for real-time applications. A two-step root multiple signal classification (TS-root-MUSIC) algorithm is proposed to avoid the complex EVD/SVD computation using a uniform linear array (ULA) based on a mild assumption that the number of signals is less than half that of sensors. The ULA is divided into two subarrays, and three noise-free cross-correlation matrices are constructed using data collected by the two subarrays. A low-complexity linear operation is derived to obtain a rough noise subspace for a first-step DOA estimate. The performance is further enhanced in the second step by using the first-step result to renew the previous estimated noise subspace with a slightly increased complexity. The new technique can provide close root mean square error (RMSE) performance to root-MUSIC with reduced computational burden, which are verified by numerical simulations.

Super-Resolution Time-Delays Estimation in HF Channel Sounding Based on Chirp Sequence

2011 ◽  
Vol 204-210 ◽  
pp. 2133-2139
Author(s):  
Long Fei Fu ◽  
Gang Xin ◽  
Shui Lian Zhang
Keyword(s):  
Time Delay ◽  
Time Delays ◽  
Measurement Data ◽  
Super Resolution ◽  
Wide Band ◽  
Estimation Problem ◽  
Delay Model ◽  
Music Algorithm ◽  
Problem Of Time ◽  
Hf Channel

According to the characteristics of HF channel and chirp signal, an innovative multipath time-delay model of wide-band HF channel was proposed, by which the estimation problem of time-delay was converted into an estimation problem of spectrum.Then the MUSIC algorithm with super-resolution ability was applied to the problem above. The feasibility of estimating multipath time-delays based on single measurement data was deeply discussed. Meanwhile, the performance of applying MUSIC and root MUSIC algorithm to the model proposed in the paper was presented. The simulation results suggested that the method proposed in the paper owned super-resolution ability and robust in estimation of multipath time-delay.

scholarly journals On range super - resolution in a radar with many carriers. Simulation and experiment

2019 ◽  
pp. 24-29
Author(s):  
I. L. Nagornykh ◽  
N. D. Bazhenov
Keyword(s):  
Fourier Transform ◽  
Traditional Method ◽  
Super Resolution ◽  
Music Algorithm ◽  
Signal To Noise ◽  
Range Resolution ◽  
Probing Signal ◽  
Simulation And Experiment ◽  
The Fourier Transform ◽  
Research Show

The paper focuses on radar operation and the results of its simulation. The probing signal of the radar is a set of 16 orthogonal carriers. To determine the range in such radar, the MUSIC algorithm was applied, which relates to super - resolution methods. Findings of research show that the MUSIC algorithm makes it possible to increase the radar range resolution in the signal - to-noise 0-20 dB ratio by 4-8 times as compared with the traditional method based on the Fourier transform. The developed models were experimentally verified

scholarly journals Low-Complexity Super-Resolution Detection for Range-Vital Doppler Estimation FMCW Radar

2021 ◽  
Vol 21 (3) ◽  
pp. 236-245
Author(s):  
Bongseok Kim ◽  
Youngseok Jin ◽  
Youngdoo Choi ◽  
Jonghun Lee ◽  
Sangdong Kim
Keyword(s):  
Continuous Wave ◽  
Low Cost ◽  
Phase Variation ◽  
Super Resolution ◽  
Estimation Algorithm ◽  
Low Complexity ◽  
Music Algorithm ◽  
Fmcw Radar ◽  
Detection Algorithms ◽  
Simulation And Experiment

This paper proposes low-complexity super-resolution detection for range-vital Doppler estimation frequency-modulated continuous wave (FMCW) radar. In regards to vital radar, and in order to estimate joint range and vital Doppler information such as the human heartbeat and respiration, two-dimensional (2D) detection algorithms such as 2D-FFT (fast Fourier transform) and 2D-MUSIC (multiple signal classification) are required. However, due to the high complexity of 2D full-search algorithms, it is difficult to apply this process to low-cost vital FMCW systems. In this paper, we propose a method to estimate the range and vital Doppler parameters by using 1D-FFT and 1D-MUSIC algorithms, respectively. Among 1D-FFT outputs for range detection, we extract 1D-FFT results based solely on human target information with phase variation of respiration for each chirp; subsequently, the 1D-MUSIC algorithm is employed to obtain accurate vital Doppler results. By reducing the dimensions of the estimation algorithm from 2D to 1D, the computational burden is reduced. In order to verify the performance of the proposed algorithm, we compare the Monte Carlo simulation and root-mean-square error results. The simulation and experiment results show that the complexity of the proposed algorithm is significantly lower than that of an algorithm detecting signals in several regions.

Super-resolution ISAR imaging based on the modified iterative reweighted approach

2019 ◽  
Vol 13 (03) ◽  
pp. 1
Author(s):  
Yu Xiao ◽  
Zhenghong Deng ◽  
Xingyu He
Keyword(s):  
Super Resolution ◽  
Isar Imaging

scholarly journals Bistatic ISAR Imaging with a V-FM Waveform Based on a Dual-Channel-Coupled 2D-CS Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3082 ◽  
Author(s):  
Jiyuan Chen ◽  
Xiaoyi Pan ◽  
Letao Xu ◽  
Wei Wang
Keyword(s):  
Compressed Sensing ◽  
Optimization Problem ◽  
Super Resolution ◽  
Space Distribution ◽  
Signal Model ◽  
Low Snr ◽  
Dual Channel ◽  
Isar Imaging ◽  
Radar Echo ◽  
Resolution Model

Due to the sparsity of the space distribution of point scatterers and radar echo data, the theory of Compressed Sensing (CS) has been successfully applied in Inverse Synthetic Aperture Radar (ISAR) imaging, which can recover an unknown sparse signal from a limited number of measurements by solving a sparsity-constrained optimization problem. In this paper, since the V style modulation(V-FM) signal can mitigate the ambiguity apparent in range and velocity, the dual-channel, two-dimension, compressed-sensing (2D-CS) algorithm is proposed for Bistatic ISAR (Bi-ISAR) imaging, which directly deals with the 2D signal model for image reconstruction based on solving a nonconvex optimization problem. The coupled 2D super-resolution model of the target’s echoes is firstly established; then, the 2D-SL0 algorithm is applied in each channel with different dictionaries, and the final image is obtained by synthesizing the two channels. Experiments are used to test the robustness of the Bi-ISAR imaging framework with the two-dimensional CS method. The results show that the framework is capable accurately reconstructing the Bi-ISAR image within the conditions of low SNR and low measured data.

Harmonic Frequency Estimation Based on Modified-MUSIC Algorithm in Power System

2015 ◽  
Vol 9 (1) ◽  
pp. 38-42 ◽  
Author(s):  
Xiangwen Sun ◽  
Ligong Sun
Keyword(s):  
Frequency Estimation ◽  
Estimation Method ◽  
Super Resolution ◽  
Small Data ◽  
Music Algorithm ◽  
Harmonic Frequency ◽  
Multiple Signal Classification ◽  
Analysis Techniques ◽  
Resolution Algorithm ◽  
Data Length

This paper presents a new harmonics frequency estimation method. Unlike the conventional harmonic frequency estimation method (fast Fourier transform), the new algorithm is based on spectrum analysis techniques often used to estimate the direction of angle; the most popular is the multiple signal classification (MUSIC) algorithm. The drawbacks of MUSIC algorithm are concluded. Improved-MUSIC approximation algorithm is introduced and compared with FFT based on algorithm for harmonic frequency estimation. Theoretical analysis and simulations show this algorithm is a super- resolution algorithm with small data length.

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