scholarly journals Robust Recursive Algorithm under Uncertainties via Worst-Case SINR Maximization

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xin Song ◽  
Feng Wang ◽  
Jinkuan Wang ◽  
Jingguo Ren
Keyword(s):  
Recursive Algorithm ◽  
Weight Vector ◽  
Training Data ◽  
Worst Case ◽  
Diagonal Loading ◽  
Fast Convergence Rate ◽  
The Mean ◽  
Simulation Results ◽  
Signal Steering Vector Mismatches ◽  
Sinr Maximization

The performance of traditional constrained-LMS (CLMS) algorithm is known to degrade seriously in the presence of small training data size and mismatches between the assumed array response and the true array response. In this paper, we develop a robust constrained-LMS (RCLMS) algorithm based on worst-case SINR maximization. Our algorithm belongs to the class of diagonal loading techniques, in which the diagonal loading factor is obtained in a simple form and it decreases the computation cost. The updated weight vector is derived by the descent gradient method and Lagrange multiplier method. It demonstrates that our proposed recursive algorithm provides excellent robustness against signal steering vector mismatches and the small training data size and, has fast convergence rate, and makes the mean output array signal-to-interference-plus-noise ratio (SINR) consistently close to the optimal one. Some simulation results are presented to compare the performance of our robust algorithm with the traditional CLMS algorithm.

scholarly journals Robust adaptive beamforming with enhancing the interference suppression capability

2019 ◽  
Vol 8 ◽  
Author(s):  
Linxian Liu ◽  
Yang Li
Keyword(s):  
Performance Optimization ◽  
Signal To Noise Ratio ◽  
Interference Suppression ◽  
Adaptive Beamforming ◽  
Training Data ◽  
Worst Case ◽  
Diagonal Loading ◽  
Steering Vector ◽  
Robust Adaptive Beamforming ◽  
Robust Adaptive

AbstractThe steering vector mismatch causes signal self-nulling for adaptive beamforming when the training data contain the desired signal component. To prevent signal self-nulling, many beamformers use robust technology, which is usually equivalent to the diagonal loading approach. Unfortunately, the diagonal loading approach achieves better signal enhancement at the cost of losing its interference suppression capability, especially at high input signal-to-noise ratio. In this paper, a novel robust adaptive beamforming method is developed to improve the interference suppression capability. The proposed beamformer is based on the worst-case performance optimization technology with a new estimated steering vector and a special set parameter. Firstly, a subspace which is orthogonal to the interference's steering vector is obtained by using the interference-plus-noise covariance matrix; then a new steering vector which is orthogonal to each interference's steering vector is estimated; finally, the beamformer's weight is solved with the worst-case performance optimization technology with a special set parameter. Theoretical analysis of the interference suppression principle is analyzed in detail, and some simulation results are presented to evaluate the performance of the proposed beamformer.

scholarly journals Doubly Constrained Robust Blind Beamforming Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Xin Song ◽  
Jingguo Ren ◽  
Qiuming Li
Keyword(s):  
Performance Optimization ◽  
Computational Cost ◽  
Taylor Series Expansion ◽  
Weight Vector ◽  
Constant Modulus Algorithm ◽  
Worst Case ◽  
Steering Vector ◽  
Complexity Cost ◽  
Signal Steering Vector Mismatches ◽  
Output Sinr

We propose doubly constrained robust least-squares constant modulus algorithm (LSCMA) to solve the problem of signal steering vector mismatches via the Bayesian method and worst-case performance optimization, which is based on the mismatches between the actual and presumed steering vectors. The weight vector is iteratively updated with penalty for the worst-case signal steering vector by the partial Taylor-series expansion and Lagrange multiplier method, in which the Lagrange multipliers can be optimally derived and incorporated at each step. A theoretical analysis for our proposed algorithm in terms of complexity cost, convergence performance, and SINR performance is presented in this paper. In contrast to the linearly constrained LSCMA, the proposed algorithm provides better robustness against the signal steering vector mismatches, yields higher signal captive performance, improves greater array output SINR, and has a lower computational cost. The simulation results confirm the superiority of the proposed algorithm on beampattern control and output SINR enhancement.

Research on the White Noise Suppression by Adaptive Filtering of Genetic Algorithm

2012 ◽  
Vol 155-156 ◽  
pp. 989-994 ◽  
Author(s):  
Xiao Rong Tong
Keyword(s):  
Genetic Algorithm ◽  
White Noise ◽  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Noise Suppression ◽  
Signal Transmission ◽  
Weight Vector ◽  
Mean Filter ◽  
The Mean ◽  
Simulation Results

Signal transmission is often subject to the disturbance of white noise. Owing to the spectrum of white noise can be found in the real number field, it is often difficult to filter out with the traditional filter. This article describes the methods of white noise suppression using adaptive filter and mean filter. First, using the genetic algorithm to optimize the weight vector of the adaptive filter, and then using the method of the mean filter to further filter, Simulation results show that the filter can effectively suppress white noise.

A Robust Eigenanalysis Interference Canceler Using Sensitivity-Based Number of Sources Estimation

2019 ◽  
Vol 33 (05) ◽  
pp. 1958007
Author(s):  
Bingfeng Qian ◽  
Yize Sun
Keyword(s):  
Weight Vector ◽  
Adaptive Beamforming ◽  
Sensitivity Function ◽  
Training Data ◽  
Eigenvalue Decomposition ◽  
Adaptive Weight ◽  
Weight Calculation ◽  
Minimum Sensitivity ◽  
Simulation Results ◽  
Expected Signal

When the estimation of the number of sources is inaccurate and the expected signal is mixed in the training data, the conventional eigenanalysis interference canceler (EC) will result in the expected signal cancelation, which leads the performance of adaptive beamforming to be degraded significantly. In this paper, a robust eigenanalysis interference canceler (REC) algorithm is proposed to resolve this problem by using the minimum sensitivity-based number of sources estimation. In the proposed method, the number of sources is first pre-estimated by effectively using the minimum sensitivity function. Then, based on the eigenvalue decomposition of the array covariance matrix, the expected signal in the training data is determined and eliminated, so that the corresponding impact of the adaptive weight calculation can be avoided. Finally, the adaptive weight vector is obtained by orthogonally projecting the quiescent weight vector into the interference subspace. Simulation results show that the performance of the output signal-to-interference-plus-noise ratio (SINR) of the proposed algorithm has improved significantly when compared with the conventional adaptive beamforming methods.

scholarly journals Characterization of Deleterious Mutations in Outcrossing Populations

Genetics ◽  
1998 ◽  
Vol 150 (2) ◽  
pp. 945-956 ◽  
Author(s):  
Hong-Wen Deng
Keyword(s):  
Genetic Variation ◽  
Genetic Variance ◽  
Estimation Bias ◽  
Deleterious Mutations ◽  
Environmental Variance ◽  
Higher Power ◽  
Sib Mating ◽  
The Mean ◽  
Simulation Results

Abstract Deng and Lynch recently proposed estimating the rate and effects of deleterious genomic mutations from changes in the mean and genetic variance of fitness upon selfing/outcrossing in outcrossing/highly selfing populations. The utility of our original estimation approach is limited in outcrossing populations, since selfing may not always be feasible. Here we extend the approach to any form of inbreeding in outcrossing populations. By simulations, the statistical properties of the estimation under a common form of inbreeding (sib mating) are investigated under a range of biologically plausible situations. The efficiencies of different degrees of inbreeding and two different experimental designs of estimation are also investigated. We found that estimation using the total genetic variation in the inbred generation is generally more efficient than employing the genetic variation among the mean of inbred families, and that higher degree of inbreeding employed in experiments yields higher power for estimation. The simulation results of the magnitude and direction of estimation bias under variable or epistatic mutation effects may provide a basis for accurate inferences of deleterious mutations. Simulations accounting for environmental variance of fitness suggest that, under full-sib mating, our extension can achieve reasonably well an estimation with sample sizes of only ∼2000-3000.

scholarly journals Robust Adaptive Filtering Algorithm for Self-Interference Cancellation with Impulsive Noise

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 196
Author(s):  
Jun Lu ◽  
Qunfei Zhang ◽  
Wentao Shi ◽  
Lingling Zhang ◽  
Juan Shi
Keyword(s):  
Adaptive Filtering ◽  
Interference Cancellation ◽  
Impulsive Noise ◽  
Weight Vector ◽  
Filtering Algorithm ◽  
Convergence Performance ◽  
Simulation Results ◽  
Robust Adaptive ◽  
Arctangent Cost Function ◽  
Simultaneous Transmission

Self-interference (SI) is usually generated by the simultaneous transmission and reception in the same system, and the variable SI channel and impulsive noise make it difficult to eliminate. Therefore, this paper proposes an adaptive digital SI cancellation algorithm, which is an improved normalized sub-band adaptive filtering (NSAF) algorithm based on the sparsity of the SI channel and the arctangent cost function. The weight vector is hardly updated when the impulsive noise occurs, and the iteration error resulting from impulsive noise is significantly reduced. Another major factor affecting the performance of SI cancellation is the variable SI channel. To solve this problem, the sparsity of the SI channel is estimated with the estimation of the weight vector at each iteration, and it is used to adjust the weight vector. Then, the convergence performance and calculation complexity are analyzed theoretically. Simulation results indicate that the proposed algorithm has better performance than the referenced algorithms.

NUMERICAL SIMULATION AND VALIDITY OF A NOVEL METHOD FOR THE PREDICTION OF ARTERY STENOSIS VIA INPUT IMPEDANCE AND SUPPORT VECTOR MACHINE

2014 ◽  
Vol 26 (01) ◽  
pp. 1450002 ◽  
Author(s):  
Hanguang Xiao
Keyword(s):  
Support Vector Machine ◽  
Input Impedance ◽  
Recursive Algorithm ◽  
Artery Stenosis ◽  
Support Vector ◽  
Arterial Tree ◽  
Novel Method ◽  
The Mean ◽  
Fold Cross Validation ◽  
Systemic Arterial Tree

The early detection and intervention of artery stenosis is very important to reduce the mortality of cardiovascular disease. A novel method for predicting artery stenosis was proposed by using the input impedance of the systemic arterial tree and support vector machine (SVM). Based on the built transmission line model of a 55-segment systemic arterial tree, the input impedance of the arterial tree was calculated by using a recursive algorithm. A sample database of the input impedance was established by specifying the different positions and degrees of artery stenosis. A SVM prediction model was trained by using the sample database. 10-fold cross-validation was used to evaluate the performance of the SVM. The effects of stenosis position and degree on the accuracy of the prediction were discussed. The results showed that the mean specificity, sensitivity and overall accuracy of the SVM are 80.2%, 98.2% and 89.2%, respectively, for the 50% threshold of stenosis degree. Increasing the threshold of the stenosis degree from 10% to 90% increases the overall accuracy from 82.2% to 97.4%. Increasing the distance of the stenosis artery from the heart gradually decreases the overall accuracy from 97.1% to 58%. The deterioration of the stenosis degree to 90% increases the prediction accuracy of the SVM to more than 90% for the stenosis of peripheral artery. The simulation demonstrated theoretically the feasibility of the proposed method for predicting artery stenosis via the input impedance of the systemic arterial tree and SVM.

DUTY-CYCLED SPINNING BASED 3D MOTION CONTROL APPROACH FOR BEVEL-TIPPED FLEXIBLE NEEDLE INSERTION

2018 ◽  
Vol 18 (07) ◽  
pp. 1840017 ◽  
Author(s):  
QIN YAO ◽  
XUMING ZHANG
Keyword(s):  
Motion Control ◽  
Control Method ◽  
Tracking Error ◽  
Open Loop ◽  
Loop Control ◽  
3D Motion ◽  
Control Approach ◽  
Target Error ◽  
The Mean ◽  
Simulation Results

Flexible needle has been widely used in the therapy delivery because it can advance along the curved lines to avoid the obstacles like important organs and bones. However, most control algorithms for the flexible needle are still limited to address its motion along a set of arcs in the two-dimensional (2D) plane. To resolve this problem, this paper has proposed an improved duty-cycled spinning based three-dimensional (3D) motion control approach to ensure that the beveled-tip flexible needle can track a desired trajectory to reach the target within the tissue. Compared with the existing open-loop duty-cycled spinning method which is limited to tracking 2D trajectory comprised of few arcs, the proposed closed-loop control method can be used for tracking any 3D trajectory comprised of numerous arcs. Distinctively, the proposed method is independent of the tissue parameters and robust to such disturbances as tissue deformation. In the trajectory tracking simulation, the designed controller is tested on the helical trajectory, the trajectory generated by rapidly-exploring random tree (RRT) algorithm and the helical trajectory. The simulation results show that the mean tracking error and the target error are less than 0.02[Formula: see text]mm for the former two kinds of trajectories. In the case of tracking the helical trajectory, the mean tracking error target error is less than 0.5[Formula: see text]mm and 1.5[Formula: see text]mm, respectively. The simulation results prove the effectiveness of the proposed method.

scholarly journals Adaptive Noise Cancellation of an Analog Fiber Optic Link

2021 ◽  
Author(s):  
Mohammad Nazrul Islam
Keyword(s):  
Optical Fiber ◽  
Fiber Optic ◽  
Signal To Noise Ratio ◽  
Optical Power ◽  
Noise Cancellation ◽  
Adaptive Noise Cancellation ◽  
Fiber Link ◽  
The Mean ◽  
Simulation Results ◽  
Adaptive Noise

There are three dominant noise mechanisms in an analog optical fiber link. These are shot noise that is proportional to the mean optical power, relative intensity noise (RIN) that is proportional to the square of the instanteaneous optical power. This report describes an adaptive noise cancellation of these dominant noise processes that persist an analog optical fiber link. The performance of an analog optical fiber link is analyzed by taking the effects of these noise processes. Analytical and simulation results show that some improvement in signal to noise ratio (SNR) and this filter is effective to remove noise adaptively from the optical fiber link.

scholarly journals Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance

2017 ◽  
Vol 4 (9) ◽  
pp. 160889 ◽  
Author(s):  
Liyan Xu ◽  
Fabing Duan ◽  
Xiao Gao ◽  
Derek Abbott ◽  
Mark D. McDonnell
Keyword(s):  
Stochastic Resonance ◽  
Recursive Algorithm ◽  
Signal Strength ◽  
Distinct Form ◽  
Mean Square ◽  
Least Mean Square ◽  
Complete Knowledge ◽  
The Mean ◽  
Suprathreshold Stochastic Resonance ◽  
Over Time

Suprathreshold stochastic resonance (SSR) is a distinct form of stochastic resonance, which occurs in multilevel parallel threshold arrays with no requirements on signal strength. In the generic SSR model, an optimal weighted decoding scheme shows its superiority in minimizing the mean square error (MSE). In this study, we extend the proposed optimal weighted decoding scheme to more general input characteristics by combining a Kalman filter and a least mean square (LMS) recursive algorithm, wherein the weighted coefficients can be adaptively adjusted so as to minimize the MSE without complete knowledge of input statistics. We demonstrate that the optimal weighted decoding scheme based on the Kalman–LMS recursive algorithm is able to robustly decode the outputs from the system in which SSR is observed, even for complex situations where the signal and noise vary over time.

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