Blind space-time channel estimation of spread-spectrum using the minimum variance approach

2002 ◽  
Author(s):  
Soohong Kim ◽  
Jonghoom Chun ◽  
Joohwan Chun
Keyword(s):  
Channel Estimation ◽  
Spread Spectrum ◽  
Minimum Variance ◽  
Space Time ◽  
Variance Approach

Improving Space-Time-Frequency MIMO-OFDM with ICI Self-Cancellation Scheme using Least Square Error Estimator

2015 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
Nur Farahiah Ibrahim ◽  
Zahari Abu Bakar ◽  
Azlina Idris
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Symbol Error Rate ◽  
Space Time ◽  
Least Square ◽  
Error Estimator ◽  
Frequency Diversity ◽  
Time Frequency ◽  
Mimo Ofdm ◽  
Subcarrier Mapping

Channel estimation techniques for Multiple-input Multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) based on comb type pilot arrangement with least-square error (LSE) estimator was investigated with space-time-frequency (STF) diversity implementation. The frequency offset in OFDM effected its performance. This was mitigated with the implementation of the presented inter-carrier interference self-cancellation (ICI-SC) techniques and different space-time subcarrier mapping. STF block coding in the system exploits the spatial, temporal and frequency diversity to improve performance. Estimated channel was fed into a decoder which combined the STF decoding together with the estimated channel coefficients using LSE estimator for equalization. The performance of the system was compared by measuring the symbol error rate with a PSK-16 and PSK-32. The results show that subcarrier mapping together with ICI-SC were able to increase the system performance. Introduction of channel estimation was also able to estimate the channel coefficient at only 5dB difference with a perfectly known channel.

scholarly journals Design of Self-Tuning Regulator for Large-Scale Interconnected Hammerstein Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Mourad Elloumi ◽  
Samira Kamoun
Keyword(s):  
Large Scale ◽  
Recursive Algorithm ◽  
Minimum Variance ◽  
Parametric Estimation ◽  
Hammerstein Model ◽  
Stochastic Nonlinear Systems ◽  
Hydraulic Systems ◽  
Self Tuning ◽  
Variance Approach ◽  
Error Method

This paper deals with the self-tuning regulator for large-scale stochastic nonlinear systems, which are composed of several interconnected nonlinear monovariable subsystems. Each interconnected subsystem is described by discrete Hammerstein model with unknown and time-varying parameters. This self-tuning control is developed on the basis of the minimum variance approach and is combined by a recursive algorithm in the estimation step. The parametric estimation step is performed on the basis of the prediction error method and the least-squares techniques. Simulation results of the proposed self-tuning regulator for two interconnected nonlinear hydraulic systems show the reliability and effectiveness of the developed method.

scholarly journals Stereo Camera Head-Eye Calibration Based on Minimum Variance Approach Using Surface Normal Vectors

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3706 ◽  
Author(s):  
Joong-Jae Lee ◽  
Mun-Ho Jeong
Keyword(s):  
Vision System ◽  
Real Data ◽  
Humanoid Robots ◽  
Calibration Method ◽  
Minimum Variance ◽  
Kinematic Data ◽  
Stereo Camera ◽  
Surface Normal ◽  
Normal Vectors ◽  
Variance Approach

This paper presents a stereo camera-based head-eye calibration method that aims to find the globally optimal transformation between a robot’s head and its eye. This method is highly intuitive and simple, so it can be used in a vision system for humanoid robots without any complex procedures. To achieve this, we introduce an extended minimum variance approach for head-eye calibration using surface normal vectors instead of 3D point sets. The presented method considers both positional and orientational error variances between visual measurements and kinematic data in head-eye calibration. Experiments using both synthetic and real data show the accuracy and efficiency of the proposed method.

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