scholarly journals Sparsity Enhanced Beamforming in The Presence of Coherent Signals

2021 ◽  
Vol 15 ◽  
pp. 55-58
Author(s):  
Bao Gen Xu ◽  
Yi He Wan ◽  
Si Long Tang ◽  
Xue Ke Ding ◽  
Qun Wan
Keyword(s):  
Sparse Representation ◽  
Minimum Variance ◽  
Beam Forming ◽  
Coherent Signals ◽  
Lp Norm ◽  
Orthogonal Relationship ◽  
Simulation Results ◽  
Norm Constraint ◽  
Signal Vector ◽  
Constraint Minimization

In order to find the directions of coherent signals, a sparsity enhanced beam-forming method is proposed. Unlike the conventional minimum variance distortless response (MVDR) method, the minimum variance in the proposed method corresponds to the orthogonal relationship between the noise subspace and the sparse representation of the received signal vector, whereas the distortless response corresponds to the nonorthogonal relationship between the signal subspace and the sparse representation of the received signal vector. The proposed sparsity enhanced MVDR (SEMVDR) method is carried out by the iterative reweighted Lp-norm constraint minimization. for direction finding of coherent signals. Simulation results are shown that SEMVDR has better performance than the existing algorithms, such as MVDR and MUSIC, when coherent signals are present.

Research of Frequency-Hopping Communication for Arbitrary Array Based on Adaptive Beam-Forming

2014 ◽  
Vol 989-994 ◽  
pp. 3693-3697
Author(s):  
Cheng Lin Qiao ◽  
Hou De Quan ◽  
Pei Zhang Cui
Keyword(s):  
Communication System ◽  
Poor Performance ◽  
Wide Band ◽  
Frequency Hopping ◽  
Minimum Variance ◽  
Beam Forming ◽  
System A ◽  
Communication Scheme ◽  
Simulation Results ◽  
Fh Communication

Aiming at the problem of poor performance of suppressing the wide band jamming and fast following jamming in Frequency-Hopping (FH) communication system, a communication scheme by combination of the complementally transformed minimum variance with FH technique for arbitrary array (CTMV-FH) is proposed, based on the purpose to maximize the output Signal to Interference and Noise Ratio (SINR). Basic theory of CTMV is introduced. The algorithm is used to suppress the wide band jamming and fast following jamming. Simulation results show that the scheme can suppress the wide band jamming and fast following jamming effectively in FH communication system.

scholarly journals Refocusing of Multiple Moving Targets Based on the Joint Sparse Processing of One Channel Synthetic Aperture Radar Imagery Patches

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1215 ◽  
Author(s):  
Xin Wang ◽  
Ling Qiao
Keyword(s):  
Synthetic Aperture Radar ◽  
Moving Objects ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Moving Targets ◽  
Lp Norm ◽  
Radar Imagery ◽  
Sar Imagery ◽  
Norm Constraint ◽  
Aperture Radar

A sparse-based refocusing methodology for multiple slow-moving targets (MTs) located inside strong clutter regions is proposed in this paper. The defocused regions of MTs in synthetic aperture radar (SAR) imagery were utilized here instead of the whole original radar data. A joint radar projection operator for the static and moving objects was formulated and employed to construct an optimization problem. The Lp norm constraint was utilized to promote the separation of MT data and the suppression of clutter. After the joint sparse imaging processing, the energy of strong static targets could be suppressed significantly in the reconstructed MT imagery. The static scene imagery could be derived simultaneously without the defocused MT. Finally, numerical simulations were used verify the validity and robustness of the proposed methodology.

scholarly journals A Closer Look at the Minimum-Variance Portfolio Optimization Model

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Zhifeng Dai
Keyword(s):  
Objective Function ◽  
Minimum Variance ◽  
Lagrangian Functions ◽  
Portfolio Strategies ◽  
Empirical Performance ◽  
Minimum Variance Portfolio ◽  
Real Market ◽  
Portfolio Optimization Model ◽  
Norm Constraint ◽  
Relationship Of

Recently, by imposing the regularization term to objective function or additional norm constraint to portfolio weights, a number of alternative portfolio strategies have been proposed to improve the empirical performance of the minimum-variance portfolio. In this paper, we firstly examine the relation between the weight norm-constrained method and the objective function regularization method in minimum-variance problems by analyzing the Karush–Kuhn–Tucker conditions of their Lagrangian functions. We give the range of parameters for the two models and the corresponding relationship of parameters. Given the range and manner of parameter selection, it will help researchers and practitioners better understand and apply the relevant portfolio models. We apply these models to construct optimal portfolios and test the proposed propositions by employing real market data.

Adaptive Beam-Forming Based on the Mutative Scale Chaos Optimization

2012 ◽  
Vol 588-589 ◽  
pp. 703-706
Author(s):  
Zhi Wei Zhang ◽  
Bo Wang ◽  
Zhi Gang Zhu
Keyword(s):  
Optimization Problem ◽  
Optimization Method ◽  
Beam Forming ◽  
Distribution Law ◽  
Sound Beam ◽  
Chaos Optimization ◽  
Vector Error ◽  
Doa Mismatch ◽  
Global Optimal ◽  
Signal Vector

In order to improve the SINR of the beam-former in the case of DOA mismatch, the paper raises the probability of constraint beam-forming algorithm based on mutative scale chaos optimization in connection with the worst performance of the best sound beam-forming algorithm. By introducing the probability constraints confidence interval and the signal vector error distribution law in the beam-forming, the constrained optimization problem is eventually turned into a nonlinear optimization problem. Then the global optimal weight is searched by the mutative scale chaos optimization method. Simulations have shown that the algorithm can significantly improve the SINR.

scholarly journals AR Model-Based Direction-of-Arrival Estimation of Coherent Signals in the Presence of Unknown Mutual Coupling

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Zhi-Chao Sha ◽  
Zhang-Meng Liu ◽  
Zhi-Tao Huang ◽  
Yi-Yu Zhou
Keyword(s):  
Mutual Coupling ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Ar Model ◽  
Estimation Accuracy ◽  
Direction Of Arrival Estimation ◽  
Coherent Signals ◽  
Model Based ◽  
Mixing Matrix ◽  
Simulation Results

This paper addresses the problem of direction-of-arrival (DOA) estimation of coherent signals in the presence of unknown mutual coupling, and an autoregression (AR) model-based method is proposed. The effects of mutual coupling can be eliminated by the inherent mechanism of the proposed algorithm, so the DOAs can be accurately estimated without any calibration sources. After the mixing matrix is estimated by independent component analysis (ICA), several parameter equations are established upon the mixing matrix. Finally, all DOAs of coherent signals are estimated by solving these equations. Compared with traditional methods, the proposed method has higher angle resolution and estimation accuracy. Simulation results demonstrate the effectiveness of the algorithm.

scholarly journals Adaptive Iterated Shrinkage Thresholding-Based Lp-Norm Sparse Representation for Hyperspectral Imagery Target Detection

2020 ◽  
Vol 12 (23) ◽  
pp. 3991
Author(s):  
Xiaobin Zhao ◽  
Wei Li ◽  
Mengmeng Zhang ◽  
Ran Tao ◽  
Pengge Ma
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Matched Filter ◽  
Hyperspectral Imagery ◽  
Low Rank ◽  
Collaborative Representation ◽  
Support Vector ◽  
L1 Norm ◽  
Lp Norm ◽  
Norm Minimization

In recent years, with the development of compressed sensing theory, sparse representation methods have been concerned by many researchers. Sparse representation can approximate the original image information with less space storage. Sparse representation has been investigated for hyperspectral imagery (HSI) detection, where approximation of testing pixel can be obtained by solving l1-norm minimization. However, l1-norm minimization does not always yield a sufficiently sparse solution when a dictionary is not large enough or atoms present a certain level of coherence. Comparatively, non-convex minimization problems, such as the lp penalties, need much weaker incoherence constraint conditions and may achieve more accurate approximation. Hence, we propose a novel detection algorithm utilizing sparse representation with lp-norm and propose adaptive iterated shrinkage thresholding method (AISTM) for lp-norm non-convex sparse coding. Target detection is implemented by representation of the all pixels employing homogeneous target dictionary (HTD), and the output is generated according to the representation residual. Experimental results for four real hyperspectral datasets show that the detection performance of the proposed method is improved by about 10% to 30% than methods mentioned in the paper, such as matched filter (MF), sparse and low-rank matrix decomposition (SLMD), adaptive cosine estimation (ACE), constrained energy minimization (CEM), one-class support vector machine (OC-SVM), the original sparse representation detector with l1-norm, and combined sparse and collaborative representation (CSCR).

scholarly journals Inverse spectral decomposition using an lp-norm constraint for the detection of close geological anomalies

2020 ◽  
Vol 17 (6) ◽  
pp. 1463-1477 ◽  
Author(s):  
San-Yi Yuan ◽  
Shan Yang ◽  
Tie-Yi Wang ◽  
Jie Qi ◽  
Shang-Xu Wang
Keyword(s):  
Spectral Decomposition ◽  
Frequency Resolution ◽  
Space Resolution ◽  
Time Frequency ◽  
Lp Norm ◽  
Geological Features ◽  
Full Band ◽  
Spectral Coefficients ◽  
Norm Constraint ◽  
Compact Spectrum

AbstractAn important application of spectral decomposition (SD) is to identify subsurface geological anomalies such as channels and karst caves, which may be buried in full-band seismic data. However, the classical SD methods including the wavelet transform (WT) are often limited by relatively low time–frequency resolution, which is responsible for false high horizon-associated space resolution probably indicating more geological structures, especially when close geological anomalies exist. To address this issue, we impose a constraint of minimizing an lp (0 < p < 1) norm of time–frequency spectral coefficients on the misfit derived by using the inverse WT and apply the generalized iterated shrinkage algorithm to invert for the optimal coefficients. Compared with the WT and inverse SD (ISD) using a typical l1-norm constraint, the modified ISD (MISD) using an lp-norm constraint can yield a more compact spectrum contributing to detect the distributions of close geological features. We design a 3D synthetic dataset involving frequency-close thin geological anomalies and the other 3D non-stationary dataset involving time-close anomalies to demonstrate the effectiveness of MISD. The application of 4D spectrum on a 3D real dataset with an area of approximately 230 km2 illustrates its potential for detecting deep channels and the karst slope fracture zone.

Group-Based Sparse Representation Based on lp-norm Minimization for Compressive Sensing

2020 ◽  
Vol 8 (1) ◽  
pp. 13-18
Author(s):  
Ruijing Li ◽  
◽  
Yechao Bai ◽  
Xinggan Zhang ◽  
Lan Tang ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Sparse Representation ◽  
Lp Norm ◽  
Norm Minimization

Efficient Beam-Forming for Maximum Outage of Cognitive Relay Networks

2014 ◽  
Vol 926-930 ◽  
pp. 2321-2324
Author(s):  
Zi Jun Liu ◽  
Zhan Gao ◽  
Guo Xin Li ◽  
Hai Tao Zhang
Keyword(s):  
Mean Square Error ◽  
Multiple Antennas ◽  
Minimum Mean Square Error ◽  
Relay Networks ◽  
Beam Forming ◽  
Mean Square ◽  
Cognitive Relay Networks ◽  
Optimal Beam ◽  
Simulation Results ◽  
Theoretical Results

We consider the scenario of cognitive relay networks, where the cognitive relay is equipped with multiple antennas and the cognitive destinations have only one antenna due to the size and cost limitations. Aiming to maximize the signal-to-interference noise ratio (SINR), we develop the optimal beam-forming scheme for the relay case. The proposed scheme is based on minimum mean square error (MMSE). The theoretical results are validated by simulations. Simulation results show that the proposed scheme has a considerable performance.

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