Search for: least-mean-square-error
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    How to synchronize and register an optical-inertial tracking system

    , Article Applied Mechanics and Materials ; Volume 332 , 2013 , Pages 130-136 ; 16609336 (ISSN) ; 9783037857335 (ISBN) Soroush, A ; Akbar, M ; Farahmand, F ; Sharif University of Technology
    Multi-sensor tracking is widely used for augmentation of tracking accuracy using data fusion. A basic requirement for such applications is the real time temporal synchronization and spatial registration of two sensory data. In this study a new method for time and space coordination of two tracking sensor measurements has been presented. For spatial registration we used a body coordinate system and then applied the effect of the level arm. The time synchronization was done based on least mean square (LMS) error method. This method was implemented to synchronize the position and orientation of an object using Inertial (1IMU) and Optical (Optotrak) tracking systems. The results of synchronized... 

    Joint time difference of arrival/angle of arrival position finding in passive radar

    , Article IET Radar, Sonar and Navigation ; Volume 3, Issue 2 , 2009 , Pages 167-176 ; 17518784 (ISSN) Norouzi, Y ; Derakhshani, M ; Sharif University of Technology
    Several target position finding methods are proposed in various papers mainly regarding sensor networks. However, the problem of position finding in passive radar systems is somewhat different from the general case of sensor networks. Generally, in a passive radar system, there are few receivers located at short distances when compared with the it distance from the target. In this case, a problem known as geometric dilution of precision (GDP) occurs, which considerably increases the error of many proposed methods. This phenomenon can even make the position finding equations non-solvable. Regarding this fact, we have developed a least mean square error (LMSE) based method, which uses both the... 

    Estimation of effective brain connectivity with dual kalman filter and EEG source localization methods

    , Article Australasian Physical and Engineering Sciences in Medicine ; Volume 40, Issue 3 , 2017 , Pages 675-686 ; 01589938 (ISSN) Rajabioun, M ; Motie Nasrabadi, A ; Shamsollahi, M. B ; Sharif University of Technology
    Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective...