On the effect of input signal correlation on weight misadjustment in the RLS algorithm

1995 ◽  
Vol 43 (4) ◽  
pp. 988-991 ◽  
Author(s):  
T. Adali ◽  
S.H. Ardalan
Keyword(s):  
Input Signal ◽  
Rls Algorithm ◽  
Signal Correlation

Research on TWACS Interference Suppression Based on the RLS Algorithm

2012 ◽  
Vol 229-231 ◽  
pp. 1560-1563
Author(s):  
Min Chen ◽  
Wen Bing Lu ◽  
Chong Zhang ◽  
Liang Zhang
Keyword(s):  
Adaptive Filter ◽  
Input Signal ◽  
Filter Design ◽  
Negative Impact ◽  
Spectral Characteristics ◽  
Reference Signal ◽  
Statistical Characteristics ◽  
Good Effect ◽  
Rls Algorithm ◽  
Strong Interference

TWACS (Two Way Automatic Communication System) has advantages such as economical and practical use, strong interference capability, long transmission distance and so on. But the noise in China's industrial distribution power grid owns the characters of strong interference randomness and even annihilation of the modulated signal. So the noise has a negative impact on power line communication performance. While the adaptive filter does not need to know in advance on the statistical characteristics of the input signal and noise, and has nothing to do with the spectral characteristics of the input signal. Based on this, it’s premised that the modulation signal is basically known, and the signal beyond modulation domain is regarded as reference signal to design an adaptive filter applied to TWACS by referring to the block diagram of the adaptive filter model to adjust the variable parameters of the RLS algorithm. Finally, the field test signal simulations show good effect of the adaptive filter design

scholarly journals Structure and mathematical model of gradient jammer cancellers

2019 ◽  
Vol 62 (6) ◽  
pp. 763-768
Author(s):  
S. M. Kostromitski ◽  
A. P. Shumski ◽  
I. N. Davydzenka
Keyword(s):  
Mathematical Model ◽  
Input Signal ◽  
Correlation Matrix ◽  
Intermediate Model ◽  
The Mathematical Model ◽  
Signal Correlation

In the article, using subsequent transformations of the structure and mathematical model of a classic gradient jammer canceller, the mathematical model and the structure of a gradient jammer canceller with a pre-processor are obtained. A new structure provides that the adaptation speed of a canceller does not depend on the spread of the eigenvalues of the input signal correlation matrix. An intermediate model provides the analysis of weight misadjustment of the classic gradient jammer canceller. The aim of the new mathematical model is a subsequent analysis of weight misadjustment of the jammer canceller with a stable adaptation speed.

Input Signal Voltage Variation and its Effects on Non Invasive Bio-impedance Diagnosis

2011 ◽  
Vol 4 (2) ◽  
pp. 13-14
Author(s):  
Hari Krishnan G Hari Krishnan G ◽  
◽  
Dr. Ananda Natarajan R ◽  
Dr. Anima Nanda
Keyword(s):  
Input Signal ◽  
Non Invasive ◽  
Voltage Variation

THE REALIZATION OF THE ITERATIVE METHOD OF THE LEAST SQUARES FOR THE ESTIMATION OF STATIC OBJECT PARAMETERS IN MATLAB ENVIRONMENT

2017 ◽  
pp. 28-36
Author(s):  
Galina Vasil’evna Troshina ◽  
Alexander Aleksandrovich Voevoda
Keyword(s):  
Parameter Estimation ◽  
Iterative Method ◽  
Least Squares ◽  
Input Signal ◽  
Layer Structure ◽  
Estimation Procedure ◽  
Parameter Estimation Procedure ◽  
Noise Simulation ◽  
Static Object ◽  
Object Parameters

It was suggested to use the system model working in real time for an iterative method of the parameter estimation. It gives the chance to select a suitable input signal, and also to carry out the setup of the object parameters. The object modeling for a case when the system isn't affected by the measurement noises, and also for a case when an object is under the gaussian noise was executed in the MatLab environment. The superposition of two meanders with different periods and single amplitude is used as an input signal. The model represents the three-layer structure in the MatLab environment. On the most upper layer there are units corresponding to the simulation of an input signal, directly the object, the unit of the noise simulation and the unit for the parameter estimation. The second and the third layers correspond to the simulation of the iterative method of the least squares. The diagrams of the input and the output signals in the absence of noise and in the presence of noise are shown. The results of parameter estimation of a static object are given. According to the results of modeling, the algorithm works well even in the presence of significant measurement noise. To verify the correctness of the work of an algorithm the auxiliary computations have been performed and the diagrams of the gain behavior amount which is used in the parameter estimation procedure have been constructed. The entry conditions which are necessary for the work of an iterative method of the least squares are specified. The understanding of this algorithm functioning principles is a basis for its subsequent use for the parameter estimation of the multi-channel dynamic objects.

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