scholarly journals Weighted Semiparameter Model and Its Application

2014 ◽  
Vol 2014 ◽  
pp. 1-4
Author(s):  
Zhengqing Fu ◽  
Guolin Liu ◽  
Ke Zhao ◽  
Hua Guo
Keyword(s):  
Quantitative Analysis ◽  
Qualitative Analysis ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Least Square ◽  
Mean Square ◽  
Data Simulation ◽  
Main Research ◽  
True Value ◽  
Least Square Estimate

A weighted semiparameter estimate model is proposed. The parameter components and nonparameter components are weighted. The weights are determined by the characters of different data. Simulation data and real GPS data are both processed by the new model and least square estimate, ridge estimate, and semiparameter estimate. The main research method is to combine qualitative analysis and quantitative analysis. The deviation between estimated values and the true value and the estimated residuals fluctuation of different methods are used for qualitative analysis. The mean square error is used for quantitative analysis. The results of experiment show that the model has the smallest residual error and the minimum mean square error. The weighted semiparameter estimate model has effectiveness and high precision.

scholarly journals Nonlinear Least Square Based on Control Direction by Dual Method and Its Application

2016 ◽  
Vol 2016 ◽  
pp. 1-5
Author(s):  
Zhengqing Fu ◽  
Guolin Liu ◽  
Lanlan Guo ◽  
Weike Liu ◽  
Hua Guo
Keyword(s):  
Quantitative Analysis ◽  
Root Mean Square Error ◽  
Qualitative Analysis ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Least Square ◽  
Dual Method ◽  
Dual Model ◽  
Mean Square ◽  
Primal Dual

A direction controlled nonlinear least square (NLS) estimation algorithm using the primal-dual method is proposed. The least square model is transformed into the primal-dual model; then direction of iteration can be controlled by duality. The iterative algorithm is designed. The Hilbert morbid matrix is processed by the new model and the least square estimate and ridge estimate. The main research method is to combine qualitative analysis and quantitative analysis. The deviation between estimated values and the true value and the estimated residuals fluctuation of different methods are used for qualitative analysis. The root mean square error (RMSE) is used for quantitative analysis. The results of experiment show that the model has the smallest residual error and the minimum root mean square error. The new estimate model has effectiveness and high precision. The genuine data of Jining area in unwrapping experiments are used and the comparison with other classical unwrapping algorithms is made, so better results in precision aspects can be achieved through the proposed algorithm.

scholarly journals A Comprehensive Review on Channel Estimation in OFDM System

2019 ◽  
Vol 5 (3) ◽  
pp. 6 ◽  
Author(s):  
Neha Dubey ◽  
Ankit Pandit
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Orthogonal Frequency Division Multiplexing ◽  
Transmission Rate ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Least Square ◽  
Estimation Methods ◽  
Mean Square ◽  
Mimo Ofdm

In wireless communication, orthogonal frequency division multiplexing (OFDM) plays a major role because of its high transmission rate. Channel estimation and tracking have many different techniques available in OFDM systems. Among them, the most important techniques are least square (LS) and minimum mean square error (MMSE). In least square channel estimation method, the process is simple but the major drawback is it has very high mean square error. Whereas, the performance of MMSE is superior to LS in low SNR, its main problem is it has high computational complexity. If the error is reduced to a very low value, then an exact signal will be received. In this paper an extensive review on different channel estimation methods used in MIMO-OFDM like pilot based, least square (LS) and minimum mean square error method (MMSE) and least minimum mean square error (LMMSE) methods and also other channel estimation methods used in MIMO-OFDM are discussed.

scholarly journals Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems

2018 ◽  
Vol 8 (9) ◽  
pp. 1607 ◽  
Author(s):  
Xiao Zhou ◽  
Chengyou Wang ◽  
Ruiguang Tang ◽  
Mingtong Zhang
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Confidence Level ◽  
Orthogonal Frequency Division Multiplexing ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Least Square ◽  
Estimation Methods ◽  
Mean Square ◽  
Inverse Operation

Channel estimation is an important module for improving the performance of the orthogonal frequency division multiplexing (OFDM) system. The pilot-based least square (LS) algorithm can improve the channel estimation accuracy and the symbol error rate (SER) performance of the communication system. In pilot-based channel estimation, a certain number of pilots are inserted at fixed intervals between OFDM symbols to estimate the initial channel information, and channel estimation results can be obtained by one-dimensional linear interpolation. The minimum mean square error (MMSE) and linear minimum mean square error (LMMSE) algorithms involve the inverse operation of the channel matrix. If the number of subcarriers increases, the dimension of the matrix becomes large. Therefore, the inverse operation is more complex. To overcome the disadvantages of the conventional channel estimation methods, this paper proposes a novel OFDM channel estimation method based on statistical frames and the confidence level. The noise variance in the estimated channel impulse response (CIR) can be largely reduced under statistical frames and the confidence level; therefore, it reduces the computational complexity and improves the accuracy of channel estimation. Simulation results verify the effectiveness of the proposed channel estimation method based on the confidence level in time-varying dynamic wireless channels.

scholarly journals تقدير دالة الأنحدار اللامعلمي باستخدام بعض الطرائق اللامعلمية الرتيبة

2008 ◽  
Vol 14 (50) ◽  
pp. 304
Author(s):  
ياسمين عبد الرحمن محمد ◽  
دجلة ابراهيم مهدي
Keyword(s):  
Mean Square Error ◽  
Monotone Function ◽  
Minimum Mean Square Error ◽  
Kernel Estimator ◽  
Regression Function ◽  
Least Square ◽  
Maximum Efficiency ◽  
Mean Square ◽  
Statistical Measures ◽  
Monotone Methods

This research was concerning to study monotone nonparametric methods for estimating the nonparametric regression function (i.e treatment outlier) to achieve a monotone function (increasing or decreasing). So we will use the monotone methods to treatment outlier but after estimate the regression function with use kernel estimator (Nadarya - Watson) these methods are:- 1- Mukerjee method takes averages of maximums and minimum of subsets of the data was used to adjust the initial kernel regression estimates and use the researcher special case when . 2- Algorithm least square isotonic regression. In the experimental aspect comparison was done of which is the best methods through the simulation procedure using Mote Carlo method using five models. While in the application aspect practical application was done on data represent the measurements for blood pressure patients. In both aspects we use two of the important statistical measures which are Mean square error (MSE) and efficiency. We find through the application that the best method is Mukerjee method for general case as it has minimum Mean square error and maximum efficiency.  

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
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