System Identification with the Wiener Filter

2011 ◽  
pp. 13-27
Author(s):  
Jacob Benesty ◽  
Constantin Paleologu ◽  
Tomas Gänsler ◽  
Silviu Ciochină
Keyword(s):  
System Identification ◽  
Wiener Filter

scholarly journals Identification of Linear and Bilinear Systems: A Unified Study

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1790
Author(s):  
Jacob Benesty ◽  
Constantin Paleologu ◽  
Laura-Maria Dogariu ◽  
Silviu Ciochină
Keyword(s):  
System Identification ◽  
Wiener Filter ◽  
Low Rank ◽  
Identification Problems ◽  
Low Rank Approximation ◽  
Product Decomposition ◽  
Single Output ◽  
Unified Study ◽  
Recent Developments ◽  
Improved Performance

System identification problems are always challenging to address in applications that involve long impulse responses, especially in the framework of multichannel systems. In this context, the main goal of this review paper is to promote some recent developments that exploit decomposition-based approaches to multiple-input/single-output (MISO) system identification problems, which can be efficiently solved as combinations of low-dimension solutions. The basic idea is to reformulate such a high-dimension problem in the framework of bilinear forms, and to then take advantage of the Kronecker product decomposition and low-rank approximation of the spatiotemporal impulse response of the system. The validity of this approach is addressed in terms of the celebrated Wiener filter, by developing an iterative version with improved performance features (related to the accuracy and robustness of the solution). Simulation results support the main theoretical findings and indicate the appealing performance of these developments.

scholarly journals Application of powerline noise cancellation method in correlation identification of controlled source electromagnetic method

2021 ◽  
Vol 18 (3) ◽  
pp. 339-354
Author(s):  
Zhi Yang ◽  
Jingtian Tang ◽  
Xiao Xiao ◽  
Qiyun Jiang ◽  
Xiangyu Huang ◽  
...  
Keyword(s):  
System Identification ◽  
Estimation Method ◽  
Wiener Filter ◽  
Noise Estimation ◽  
Identification Accuracy ◽  
Electromagnetic Method ◽  
Filter Method ◽  
Noise Elimination ◽  
Controlled Source ◽  
Transmitted Signal

Abstract Powerline interference in the controlled source electromagnetic method has traditionally been one of the biggest conundrums plaguing geophysicists, and its conventional denoising methods primarily include filtering and noise estimation. The filter method leaches noise at specific frequency points, which might also filter useful signals; the noise estimation method significantly eliminates interference, whereas the premise is that the noise is stable after a short time and a recorder is necessary in the field. In the present study, using the periodicity and symmetry of powerline noise, we propose a subtraction and an addition method for cancellation of the powerline noise. First, the transmitted signal is optimized so that the equivalent transmitted signal is an m sequence; then the response signal is processed by using the cancellation method; subsequently, the correlation identification is applied and finally, we solve the earth impulse response by means of the Wiener filter deconvolution method. Simulation experiments and field data tests demonstrate that the powerline noise can be well suppressed by the cancellation method proposed in the present study, so that the system identification accuracy is greatly improved. The method is simple in principle and effective in removing powerline noise, which presents a novel perspective on noise elimination for system identification.

scholarly journals A Novel Approach of Speech Recognition Using System Identification

2019 ◽  
Vol 12 (3) ◽  
pp. 72-83
Author(s):  
Ibrahim Sadoon Fatah
Keyword(s):  
Artificial Intelligence ◽  
Speech Recognition ◽  
System Identification ◽  
Wiener Filter ◽  
High Accuracy ◽  
Accurate Prediction ◽  
Novel Approach ◽  
Noisy Signals ◽  
Noisy Background

With the growing of artificial intelligence and the usage of sound commands the needs for a high accuracy speech recognition increases. Many researches are done in this area using different kinds of methods and approaches. In this research two algorithms have been introduced. The autoregressive system identification and the FIR Wiener filter. The objective of this research is to show the robustness of system identification in terms of speech recognition.Both algorithms have been implemented and tested using MATLAB where the process is done by recording full sentences from different subjects under two conditions which are clear and noisy background. For each sentence, it has been recorded two timesfor each subject; the first one was used for testing and the second sentence was used for validation. The results show that both algorithms are giving an accurate prediction when the used data are from the same subject with clear background. The advantage of system identification over the Weiner filter is shine when using noisy signals. Another advantage of using system identification for speech recognition is it can distinguish the sound difference when same sentence from different subjects is used where the Weiner filter in some cases passes them as from the same subject. This could be a huge issue if the algorithm is used for security reasons

scholarly journals System Identification Based on Tensor Decompositions: A Trilinear Approach

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 556 ◽  
Author(s):  
Laura-Maria Dogariu ◽  
Silviu Ciochină ◽  
Jacob Benesty ◽  
Constantin Paleologu
Keyword(s):  
System Identification ◽  
Nonlinear Systems ◽  
Wiener Filter ◽  
Nonlinear Behavior ◽  
Hammerstein Model ◽  
Least Mean Square ◽  
Tensor Decompositions ◽  
Single Output ◽  
Long Time ◽  
Spatiotemporal Models

The theory of nonlinear systems can currently be encountered in many important fields, while the nonlinear behavior of electronic systems and devices has been studied for a long time. However, a global approach for dealing with nonlinear systems does not exist and the methods to address this problem differ depending on the application and on the types of nonlinearities. An interesting category of nonlinear systems is one that can be regarded as an ensemble of (approximately) linear systems. Some popular examples in this context are nonlinear electronic devices (such as acoustic echo cancellers, which are used in applications for two-party or multi-party voice communications, e.g., videoconferencing), which can be modeled as a cascade of linear and nonlinear systems, similar to the Hammerstein model. Multiple-input/single-output (MISO) systems can also be regarded as separable multilinear systems and be treated using the appropriate methods. The high dimension of the parameter space in such problems can be addressed with methods based on tensor decompositions and modelling. In recent work, we focused on a particular type of multilinear structure—namely the bilinear form (i.e., two-dimensional decompositions)—in the framework of identifying spatiotemporal models. In this paper, we extend the work to the decomposition of more complex systems and we propose an iterative Wiener filter tailored for the identification of trilinear forms (where third-order tensors are involved), which can then be further extended to higher order multilinear structures. In addition, we derive the least-mean-square (LMS) and normalized LMS (NLMS) algorithms tailored for such trilinear forms. Simulations performed in the context of system identification (based on the MISO system approach) indicate the good performance of the proposed solution, as compared to conventional approaches.

scholarly journals A Kalman Filter for Multilinear Forms and Its Connection with Tensorial Adaptive Filters

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3555
Author(s):  
Laura-Maria Dogariu ◽  
Constantin Paleologu ◽  
Jacob Benesty ◽  
Cristian-Lucian Stanciu ◽  
Claudia-Cristina Oprea ◽  
...  
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Adaptive Filters ◽  
Wiener Filter ◽  
Decomposition Techniques ◽  
Identification Problems ◽  
Multilinear Forms ◽  
Wide Range ◽  
Low Dimension ◽  
The Individual

The Kalman filter represents a very popular signal processing tool, with a wide range of applications within many fields. Following a Bayesian framework, the Kalman filter recursively provides an optimal estimate of a set of unknown variables based on a set of noisy observations. Therefore, it fits system identification problems very well. Nevertheless, such scenarios become more challenging (in terms of the convergence and accuracy of the solution) when the parameter space becomes larger. In this context, the identification of linearly separable systems can be efficiently addressed by exploiting tensor-based decomposition techniques. Such multilinear forms can be modeled as rank-1 tensors, while the final solution is obtained by solving and combining low-dimension system identification problems related to the individual components of the tensor. Recently, the identification of multilinear forms was addressed based on the Wiener filter and most well-known adaptive algorithms. In this work, we propose a tensorial Kalman filter tailored to the identification of multilinear forms. Furthermore, we also show the connection between the proposed algorithm and other tensor-based adaptive filters. Simulation results support the theoretical findings and show the appealing performance features of the proposed Kalman filter for multilinear forms.

scholarly journals An Insightful Overview of the Wiener Filter for System Identification

2021 ◽  
Vol 11 (17) ◽  
pp. 7774
Author(s):  
Laura-Maria Dogariu ◽  
Jacob Benesty ◽  
Constantin Paleologu ◽  
Silviu Ciochină
Keyword(s):  
System Identification ◽  
Real World ◽  
Optimization Problems ◽  
Wiener Filter ◽  
Identification Problem ◽  
Low Rank ◽  
Regularization Methods ◽  
Noisy Environments ◽  
Regularization Technique ◽  
Theoretical Developments

Efficiently solving a system identification problem represents an important step in numerous important applications. In this framework, some of the most popular solutions rely on the Wiener filter, which is widely used in practice. Moreover, it also represents a benchmark for other related optimization problems. In this paper, new insights into the regularization of the Wiener filter are provided, which is a must in real-world scenarios. A proper regularization technique is of great importance, especially in challenging conditions, e.g., when operating in noisy environments and/or when only a low quantity of data is available for the estimation of the statistics. Different regularization methods are investigated in this paper, including several new solutions that fit very well for the identification of sparse and low-rank systems. Experimental results support the theoretical developments and indicate the efficiency of the proposed techniques.

Density-based discrimination of protein and RNA in the ribosome

1992 ◽  
Vol 50 (1) ◽  
pp. 464-465
Author(s):  
Joachim Frank
Keyword(s):  
Transfer Function ◽  
Spatial Frequency ◽  
Three Dimensional ◽  
Wiener Filter ◽  
Dark Field Image ◽  
Dark Field ◽  
Frequency Range ◽  
Bright Field ◽  
Contrast Transfer Function ◽  
Pass Filter

Cryo-electron microscopy combined with single-particle reconstruction techniques has allowed us to form a three-dimensional image of the Escherichia coli ribosome.In the interior, we observe strong density variations which may be attributed to the difference in scattering density between ribosomal RNA (rRNA) and protein. This identification can only be tentative, and lacks quantitation at this stage, because of the nature of image formation by bright field phase contrast. Apart from limiting the resolution, the contrast transfer function acts as a high-pass filter which produces edge enhancement effects that can explain at least part of the observed variations. As a step toward a more quantitative analysis, it is necessary to correct the transfer function in the low-spatial-frequency range. Unfortunately, it is in that range where Fourier components unrelated to elastic bright-field imaging are found, and a Wiener-filter type restoration would lead to incorrect results. Depending upon the thickness of the ice layer, a varying contribution to the Fourier components in the low-spatial-frequency range originates from an “inelastic dark field” image. The only prospect to obtain quantitatively interpretable images (i.e., which would allow discrimination between rRNA and protein by application of a density threshold set to the average RNA scattering density may therefore lie in the use of energy-filtering microscopes.

A Community Based High Risk Register for Hearing Loss

1982 ◽  
Vol 47 (4) ◽  
pp. 373-375 ◽  
Author(s):  
James L. Fitch ◽  
Thomas F. Williams ◽  
Josephine E. Etienne
Keyword(s):  
Hearing Loss ◽  
System Identification ◽  
High Risk ◽  
Language Learning ◽  
Education Program ◽  
Communication System ◽  
Auditory Signal ◽  
Early Age ◽  
Community Based ◽  
Children With Hearing Loss

The critical need to identify children with hearing loss and provide treatment at the earliest possible age has become increasingly apparent in recent years (Northern & Downs, 1978). Reduction of the auditory signal during the critical language-learning period can severely limit the child's potential for developing a complete, effective communication system. Identification and treatment of children having handicapping conditions at an early age has gained impetus through the Handicapped Children's Early Education Program (HCEEP) projects funded by the Bureau of Education for the Handicapped (BEH).

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