scholarly journals Multipath Separation and Parameter Estimation by Single RIS Antenna in Fading Channel

2021 ◽  
Author(s):  
Yangming Lou
Keyword(s):  
Parameter Estimation ◽  
Electromagnetic Waves ◽  
Signal Transmission ◽  
Wireless Channel ◽  
Parameter Estimates ◽  
Angle Of Arrival ◽  
Pilot Symbol ◽  
Communication Performance ◽  
Metamaterial Antenna ◽  
Path Gain

Channel identification and measurement accuracywill greatly affect wireless communication performance. Thebroadcasting of electromagnetic waves will produce multipath,and its superposition will lead to fading. If the multipath canbe separated and the phase of each path can be adjustedseparately, better signal transmission and reception strategiescan be designed to weaken or eliminate fading and improvesignal transmission performance. In this paper, we first analyzethe multipath structure of the wireless channel, then utilize thedynamic isomeric characteristics of the reconfigurable intelligentsurface (RIS) antenna, also called dynamic metamaterial antenna(DMA), to form differentiated patterns, and rapidly sampleswithin a period of single pilot symbol, so that a group ofmultipath signals are projected on multiple patterns and pathscan be separated and measured. We simulated and evaluated the normalized mean square error of the parameter estimates, including angle of arrival and path gain of channels underdifferent conditions. The results demonstrate that even single1-bi coding RIS antenna can achieve well multipath separationand parameter estimation effects.

scholarly journals Multipath Separation and Parameter Estimation by Single RIS Antenna in Fading Channel

2021 ◽  
Author(s):  
Yangming Lou
Keyword(s):  
Parameter Estimation ◽  
Electromagnetic Waves ◽  
Signal Transmission ◽  
Wireless Channel ◽  
Parameter Estimates ◽  
Angle Of Arrival ◽  
Pilot Symbol ◽  
Communication Performance ◽  
Metamaterial Antenna ◽  
Path Gain

Channel identification and measurement accuracywill greatly affect wireless communication performance. Thebroadcasting of electromagnetic waves will produce multipath,and its superposition will lead to fading. If the multipath canbe separated and the phase of each path can be adjustedseparately, better signal transmission and reception strategiescan be designed to weaken or eliminate fading and improvesignal transmission performance. In this paper, we first analyzethe multipath structure of the wireless channel, then utilize thedynamic isomeric characteristics of the reconfigurable intelligentsurface (RIS) antenna, also called dynamic metamaterial antenna(DMA), to form differentiated patterns, and rapidly sampleswithin a period of single pilot symbol, so that a group ofmultipath signals are projected on multiple patterns and pathscan be separated and measured. We simulated and evaluated the normalized mean square error of the parameter estimates, including angle of arrival and path gain of channels underdifferent conditions. The results demonstrate that even single1-bi coding RIS antenna can achieve well multipath separationand parameter estimation effects.

Experimental Robot Identification: Advantages of Combining Internal and External Measurements and of Using Periodic Excitation

2001 ◽  
Vol 123 (4) ◽  
pp. 630-636 ◽  
Author(s):  
Walter Verdonck ◽  
Jan Swevers ◽  
Jean-Claude Samin
Keyword(s):  
Signal Processing ◽  
Parameter Estimation ◽  
Effective Means ◽  
Base Plate ◽  
Experimental Results ◽  
Parameter Estimates ◽  
Periodic Excitation ◽  
Identification Method ◽  
Reaction Forces ◽  
Improved Model

This paper discusses the advantages of using periodic excitation and of combining internal and external measurements in experimental robot identification. This discussion is based on the robot identification method developed by Swevers et al., a method that is recognized by industry as an effective means of robot identification that is frequently used, Hirzinger, G., Fischer, M., Brunner, B., Koeppe, R., Otter, M., Grebenstein, M., and Schafer, I, 1999, “Advances is Robotics: The DLR Experiment,” The International Journal of Robotics Research, Vol. 18, No. 11, pp. 1064–1087 [3]. Experimental results on a KUKA IR 361 show that the periodicity of the robot excitation is a key element of this method. Nonperiodic robot excitation complicates the signal processing preceding the parameter estimation, often yielding less accurate parameter estimates. An extension of this identification method combines internal and external measurements, Chenut, X., Samin, J. C., Swevers, J., and Ganseman, C., 2000, “Combining Internal and External robot Models for improved Model Parameter Estimation,” Mechanical Systems and Signal Processing. Vol. 14, No. 5, pp. 691–704 [4], yielding robot models that allow to accurately predict the actuator torques and the reaction forces/torques of the robot on its base plate, which are both important for the path planning. This paper presents and critically discusses the first experimental results obtained with this method.

Parameter Estimation in a Nonlinear Vibrating System Using an Observer for an Extended System

1999 ◽  
Author(s):  
Anindya Chatterjee ◽  
Joseph P. Cusumano
Keyword(s):  
Parameter Estimation ◽  
Time Scale ◽  
Asymptotic Methods ◽  
System Response ◽  
Parameter Estimates ◽  
Fast Time ◽  
State Variables ◽  
Slow Time ◽  
Unknown Parameters ◽  
Lipschitz Continuous

Abstract We present a new observer-based method for parameter estimation for nonlinear oscillatory mechanical systems where the unknown parameters appear linearly (they may each be multiplied by bounded and Lipschitz continuous but otherwise arbitrary, possibly nonlinear, functions of the oscillatory state variables and time). The oscillations in the system may be periodic, quasiperiodic or chaotic. The method is also applicable to systems where the parameters appear nonlinearly, provided a good initial estimate of the parameter is available. The observer requires measurements of displacements. It estimates velocities on a fast time scale, and the unknown parameters on a slow time scale. The fast and slow time scales are governed by a single small parameter ϵ. Using asymptotic methods including the method of averaging, it is shown that the observer’s estimates of the unknown parameters converge like e−ϵt where t is time, provided the system response is such that the coefficient-functions of the unknown parameters are not close to being linearly dependent. It is also shown that the method is robust in that small errors in the model cause small errors in the parameter estimates. A numerical example is provided to demonstrate the effectiveness of the method.

Parameter Estimation in Marketing Models in the Presence of Influential Response Data: Robust Regression and Applications

1984 ◽  
Vol 21 (3) ◽  
pp. 268-277 ◽  
Author(s):  
Vijay Mahajan ◽  
Subhash Sharma ◽  
Yoram Wind
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Least Squares ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Regression Coefficients ◽  
Parameter Estimates ◽  
Response Data ◽  
Marketing Models ◽  
Marketing Data

In marketing models, the presence of aberrant response values or outliers in data can distort the parameter estimates or regression coefficients obtained by means of ordinary least squares. The authors demonstrate the potential usefulness of the robust regression analysis in treating influential response values in marketing data.

scholarly journals AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2727 ◽  
Author(s):  
Ruonan Zhang ◽  
Jiawei Liu ◽  
Xiaojiang Du ◽  
Bin Li ◽  
Mohsen Guizani
Keyword(s):  
Antenna Arrays ◽  
Spread Spectrum ◽  
Signal Transmission ◽  
Three Dimensional ◽  
Target Localization ◽  
Sensor Nodes ◽  
Angle Of Arrival ◽  
Transmission Scheme ◽  
Industrial Wireless Sensor Networks ◽  
Estimation Errors

High-precision and fast relative positioning of a large number of mobile sensor nodes (MSNs) is crucial for smart industrial wireless sensor networks (SIWSNs). However, positioning multiple targets simultaneously in three-dimensional (3D) space has been less explored. In this paper, we propose a new approach, called Angle-of-Arrival (AOA) based Three-dimensional Multi-target Localization (ATML). The approach utilizes two anchor nodes (ANs) with antenna arrays to receive the spread spectrum signals broadcast by MSNs. We design a multi-target single-input-multiple-output (MT-SIMO) signal transmission scheme and a simple iterative maximum likelihood estimator (MLE) to estimate the 2D AOAs of multiple MSNs simultaneously. We further adopt the skew line theorem of 3D geometry to mitigate the AOA estimation errors in determining locations. We have conducted extensive simulations and also developed a testbed of the proposed ATML. The numerical and field experiment results have verified that the proposed ATML can locate multiple MSNs simultaneously with high accuracy and efficiency by exploiting the spread spectrum gain and antenna array gain. The ATML scheme does not require extra hardware or synchronization among nodes, and has good capability in mitigating interference and multipath effect in complicated industrial environments.

Integrating Parameter Estimation Solutions From the Time and Time-Scale Domains

2009 ◽  
Author(s):  
James R. McCusker ◽  
Kourosh Danai
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Time Scale ◽  
Prediction Error ◽  
Nonlinear Least Squares ◽  
Integrated Approach ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Single Model ◽  
Iterative Estimation

A method of parameter estimation was recently introduced that separately estimates each parameter of the dynamic model [1]. In this method, regions coined as parameter signatures, are identified in the time-scale domain wherein the prediction error can be attributed to the error of a single model parameter. Based on these single-parameter associations, individual model parameters can then be estimated for iterative estimation. Relative to nonlinear least squares, the proposed Parameter Signature Isolation Method (PARSIM) has two distinct attributes. One attribute of PARSIM is to leave the estimation of a parameter dormant when a parameter signature cannot be extracted for it. Another attribute is independence from the contour of the prediction error. The first attribute could cause erroneous parameter estimates, when the parameters are not adapted continually. The second attribute, on the other hand, can provide a safeguard against local minima entrapments. These attributes motivate integrating PARSIM with a method, like nonlinear least-squares, that is less prone to dormancy of parameter estimates. The paper demonstrates the merit of the proposed integrated approach in application to a difficult estimation problem.

A Comparison of the Internal Validity of Alternative Parameter Estimation Methods in Decompositional Multiattribute Preference Models

1979 ◽  
Vol 16 (3) ◽  
pp. 313-322 ◽  
Author(s):  
Arun K. Jain ◽  
Franklin Acito ◽  
Naresh K. Malhotra ◽  
Vijay Mahajan
Keyword(s):  
Parameter Estimation ◽  
Data Collection ◽  
Data Base ◽  
Internal Validity ◽  
Detailed Comparison ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Alternative Approaches ◽  
Preference Models ◽  
Parameter Estimation Methods

Since 1971, interest in the use of decompositional multiattribute preference models in marketing has been increasing. The applications have varied in terms of the type of data used, behavior predicted, and methods used for estimating parameters. The authors examine the effect of different data collection and estimation procedures on parameter estimates and their stability and validity. An actual data base is used. A detailed comparison is made of the alternative approaches of parameter estimation and suggestions are given for the potential users of decompositional multiattribute preference models.

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