scholarly journals A Connection Between the Kalman Filter and an Optimized LMS Algorithm for Bilinear Forms

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 211 ◽  
Author(s):  
Laura-Maria Dogariu ◽  
Silviu Ciochină ◽  
Constantin Paleologu ◽  
Jacob Benesty
Keyword(s):  
Kalman Filter ◽  
Wiener Filter ◽  
Identification Problem ◽  
Lms Algorithm ◽  
Bilinear Forms ◽  
Least Mean Square ◽  
Spatiotemporal Model ◽  
Single Output ◽  
Multiple Input ◽  
Output System

The system identification problem becomes more challenging when the parameter space increases. Recently, several works have focused on the identification of bilinear forms, which are related to the impulse responses of a spatiotemporal model, in the context of a multiple-input/ single-output system. In this framework, the problem was addressed in terms of the Wiener filter and different basic adaptive algorithms. This paper studies two types of algorithms tailored for the identification of such bilinear forms, i.e., the Kalman filter (along with its simplified version) and an optimized least-mean-square (LMS) algorithm. Also, a comparison between them is performed, which shows interesting similarities. In addition to the mathematical derivation of the algorithms, we also provide extensive experimental results, which support the theoretical findings and indicate the good performance of the proposed solutions.

Convergence analysis of Bessel beamformer and its comparison with LMS in adaptive array system

2015 ◽  
Vol 34 (3) ◽  
pp. 952-961 ◽  
Author(s):  
M. Yasin ◽  
Pervez Akhtar
Keyword(s):  
Theoretical Model ◽  
Convergence Analysis ◽  
Digital Signal Processor ◽  
Wiener Filter ◽  
Digital Signal ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Content Type ◽  
Convergence Performance

Purpose – The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS) algorithm, can be named as Bessel LMS (BLMS) algorithm. Its performance is compared in adaptive environment with LMS in terms of two important performance parameters, namely; convergence and mean square error. The proposed BLMS algorithm is implemented on digital signal processor along with antenna array to make it smart in wireless sensor networks. Design/methodology/approach – Convergence analysis is theoretically developed and verified through MatLab Software. Findings – Theoretical model is verified through simulation and its results are shown in the provided table. Originality/value – The theoretical model can obtain validation from well-known result of Wiener filter theory through principle of orthogonality.

scholarly journals Optimization of Excitation Magnitudes and Phases for Maximum Efficiencies in a MISO Wireless Power Transfer System

2020 ◽  
Vol 20 (1) ◽  
pp. 16-22
Author(s):  
Hyeongwook Lee ◽  
Seunghyun Boo ◽  
Gunyoung Kim ◽  
Bomson Lee
Keyword(s):  
Wireless Power Transfer ◽  
Maximum Efficiency ◽  
Electromagnetic Simulation ◽  
Power Transfer ◽  
Wireless Power ◽  
Algorithm Optimization ◽  
Single Output ◽  
Multiple Input ◽  
Output System ◽  
Wireless Power Transfer System

This paper presents a method for solving receiver misalignment (axial or angular) problems in wireless power transfer systems using a multiple-input single-output system. The optimum magnitudes and phases of the transmitter voltages and receiver load for maximum efficiency are derived in convenient analytical forms when negligible mutual couplings between transmitters. These solutions are validated by genetic algorithm optimization and electromagnetic-simulation results for a design ex-ample of two transmitters and one rotating receiver.

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.

Vibration Identification of Gasoline Engine Part

2012 ◽  
Author(s):  
Jiuk Chang ◽  
Sangkwon Lee ◽  
Jongho Park ◽  
Byunghyun Kim ◽  
Hongseok Park
Keyword(s):  
Gasoline Engine ◽  
Coherence Function ◽  
Coherence Analysis ◽  
Vibration Source ◽  
Virtual Model ◽  
Input System ◽  
Single Output ◽  
Engine Part ◽  
Multiple Input ◽  
Output System

This paper presents the method estimating the contribution of vibration sources in gasoline engine with a multiple input system. Multi Dimensional Spectral Analysis (MDSA) has used to identify the cause of a linear dependence indicated by an ordinary coherence function. In order to apply the MDSA to the vibration source identification in gasoline engine, the virtual model of two inputs and single output system is simulated. For the validation of this model, the vibration of alternator was measured by using tri-axial accelerometers attached on the selected vibration source. After calculating the coherence analysis between each source based on the virtual model, the vibration contribution of alternator is calculated.

scholarly journals An Effective Adaptive Combination Strategy for Distributed Learning Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying
Keyword(s):  
Minimum Variance ◽  
Iteration Scheme ◽  
Estimation Problem ◽  
Lms Algorithm ◽  
Unbiased Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Combination Strategy ◽  
Adaptive Combination ◽  
Diffusion Networks

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

scholarly journals Modified GSC Method to Reduce the Distortion of the Enhanced Speech Signal Using Cross-Correlation and Sidelobe Neutralization

2021 ◽  
Vol 11 (14) ◽  
pp. 6288
Author(s):  
Hang Su ◽  
Chang-Myung Lee
Keyword(s):  
Speech Signal ◽  
Output Signal ◽  
Cross Correlation ◽  
Acoustic Noise ◽  
Audio Signal ◽  
Noise Signal ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Experiment Data ◽  
Noise Component

The generalized sidelobe canceller (GSC) method is a common algorithm to enhance audio signals using a microphone array. Distortion of the enhanced audio signal consists of two parts: the residual acoustic noise and the distortion of the desired audio signal, which means that the desired audio signal is damaged. This paper proposes a modified GSC method to reduce both kinds of distortion when the desired audio signal is a non-stationary speech signal. First, the cross-correlation coefficient between the canceling signal and the error signal of the least mean square (LMS) algorithm was added to the adaptive process of the GSC method to reduce the distortion of the enhanced signal while the energy of the desired signal frame was increased suddenly. The sidelobe pattern of beamforming was then presented to estimate the noise signal in the beamforming output signal of the GSC method. The noise component of the beamforming output signal was decreased by subtracting the estimated noise signal to improve the denoising performance of the GSC method. Finally, the GSC-SN-MCC method was proposed by merging the above two methods. The experiment was performed in an anechoic chamber to validate the proposed method in various SNR conditions. Furthermore, the simulated calculation with inaccurate noise directions was conducted based on the experiment data to inspect the robustness of the proposed method to the error of the estimated noise direction. The experiment data and calculation results indicated that the proposed method could reduce the distortion effectively under various SNR conditions and would not cause more distortion if the estimated noise direction is far from the actual noise direction.

Sign in / Sign up

Export Citation Format

Share Document