scholarly journals Water-Filling Solution for Distributed Estimation of Correlated Data in WSN MIMO System

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Ajib Setyo Arifin ◽  
Tomoaki Ohtsuki
Keyword(s):  
Mean Square Error ◽  
Channel Model ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Closed Form Solution ◽  
Distributed Estimation ◽  
Correlated Data ◽  
Multiple Access Channel ◽  
Mean Square ◽  
Water Filling

We consider the distributed estimation of a random vector signal in a power constraint wireless sensor network (WSN) that follows a multiple-input and multiple-output (MIMO) coherent multiple access channel model. We design linear coding matrices based on linear minimum mean-square error (LMMSE) fusion rule that accommodates spatial correlated data. We obtain a closed-form solution that follows a water-filling strategy. We also derive a lower bound to this model. Simulation results show that when the data is more correlated, the distortion in terms of mean-square error (MSE) degrades. By taking into account the effects of correlation, observation, and channel matrices, the proposed method performs better than equal power method.

scholarly journals Precoding Method Interference Management for Quasi-EVD Channel

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Wei Duan ◽  
Wei Song ◽  
Sang Seob Song ◽  
Moon Ho Lee
Keyword(s):  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Interference Management ◽  
Cholesky Decomposition ◽  
Multiple Access Channel ◽  
High Signal ◽  
Mean Square ◽  
Access Channel ◽  
Ber Performance

The Cholesky decomposition-block diagonalization (CD-BD) interference alignment (IA) for a multiuser multiple input multiple output (MU-MIMO) relay system is proposed, which designs precoders for the multiple access channel (MAC) by employing the singular value decomposition (SVD) as well as the mean square error (MSE) detector for the broadcast Hermitian channel (BHC) taken advantage of in our design. Also, in our proposed CD-BD IA algorithm, the relaying function is made use to restructure the quasieigenvalue decomposition (quasi-EVD) equivalent channel. This approach used for the design of BD precoding matrix can significantly reduce the computational complexity and proposed algorithm can address several optimization criteria, which is achieved by designing the precoding matrices in two steps. In the first step, we use Cholesky decomposition to maximize the sum-of-rate (SR) with the minimum mean square error (MMSE) detection. In the next step, we optimize the system BER performance with the overlap of the row spaces spanned by the effective channel matrices of different users. By iterating the closed form of the solution, we are able not only to maximize the achievable sum-of-rate (ASR), but also to minimize the BER performance at a high signal-to-noise ratio (SNR) region.

Cancelable Biometric System for face Based on linear minimum mean square error Model.

2019 ◽  
Vol 28 (1) ◽  
pp. 145-152
Author(s):  
Abd El-aziz Ebrahim Hsaneen ◽  
EL-Sayed M. El-Rabaei ◽  
Moawad I. Dessouky ◽  
Ghada El-bamby ◽  
Fathi E. Abd El-Samie ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Error Model ◽  
Mean Square ◽  
Biometric System

Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology

2021 ◽  
Vol 11 (9) ◽  
pp. 3763
Author(s):  
Yunlong Zou ◽  
Jinyu Zhao ◽  
Yuanhao Wu ◽  
Bin Wang
Keyword(s):  
Mean Square Error ◽  
Selection Process ◽  
Minimum Mean Square Error ◽  
Correlation Coefficients ◽  
False Alarms ◽  
Mean Square ◽  
Segmentation Method ◽  
Space Objects ◽  
High Earth Orbits ◽  
Star Images

Space object recognition in high Earth orbits (between 2000 km and 36,000 km) is affected by moonlight and clouds, resulting in some bright or saturated image areas and uneven image backgrounds. It is difficult to separate dim objects from complex backgrounds with gray thresholding methods alone. In this paper, we present a segmentation method of star images with complex backgrounds based on correlation between space objects and one-dimensional (1D) Gaussian morphology, and the focus is shifted from gray thresholding to correlation thresholding. We build 1D Gaussian functions with five consecutive column data of an image as a group based on minimum mean square error rules, and the correlation coefficients between the column data and functions are used to extract objects and stars. Then, lateral correlation is repeated around the identified objects and stars to ensure their complete outlines, and false alarms are removed by setting two values, the standard deviation and the ratio of mean square error and variance. We analyze the selection process of each thresholding, and experimental results demonstrate that our proposed correlation segmentation method has obvious advantages in complex backgrounds, which is attractive for object detection and tracking on a cloudy and bright moonlit night.

scholarly journals An Estimator for Weather Radar Doppler Power Spectrum via Minimum Mean Square Error

2021 ◽  
pp. 1-16
Author(s):  
Eiichi Yoshikawa ◽  
Naoya Takizawa ◽  
Hiroshi Kikuchi ◽  
Tomoaki Mega ◽  
Tomoo Ushio
Keyword(s):  
Power Spectrum ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Weather Radar ◽  
Mean Square ◽  
Doppler Power

A MODIFIED RATIO TYPE ESTIMATOR OF FINITE POPULATION MEAN UNDER STRATIFIED RANDOM SAMPLING SCHEME

2021 ◽  
pp. 58-60
Author(s):  
Naziru Fadisanku Haruna ◽  
Ran Vijay Kumar Singh ◽  
Samsudeen Dahiru
Keyword(s):  
Mean Square Error ◽  
Random Sampling ◽  
Minimum Mean Square Error ◽  
Real Life ◽  
Population Data ◽  
Auxiliary Variable ◽  
Mean Square ◽  
Data Set ◽  
Population Mean ◽  
The Mean

In This paper a modied ratio-type estimator for nite population mean under stratied random sampling using single auxiliary variable has been proposed. The expression for mean square error and bias of the proposed estimator are derived up to the rst order of approximation. The expression for minimum mean square error of proposed estimator is also obtained. The mean square error the proposed estimator is compared with other existing estimators theoretically and condition are obtained under which proposed estimator performed better. A real life population data set has been considered to compare the efciency of the proposed estimator numerically.

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