scholarly journals Data Fusion for Electromagnetic and Electrical Resistive Tomography Based on Maximum Likelihood

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Sven Nordebo ◽  
Mats Gustafsson ◽  
Therese Sjöden ◽  
Francesco Soldovieri
Keyword(s):  
Maximum Likelihood ◽  
Inverse Problems ◽  
Data Fusion ◽  
Fisher Information ◽  
Measurement Data ◽  
Problem Formulation ◽  
Truncated Singular Value Decomposition ◽  
Tunnel Detection ◽  
Maximum Likelihood Criterion ◽  
Value Decomposition

This paper presents a maximum likelihood based approach to data fusion for electromagnetic (EM) and electrical resistive (ER) tomography. The statistical maximum likelihood criterion is closely linked to the additive Fisher information measure, and it facilitates an appropriate weighting of the measurement data which can be useful with multiphysics inverse problems. The Fisher information is particularly useful for inverse problems which can be linearized similar to the Born approximation. In this paper, a proper scalar product is defined for the measurements and a truncated Singular Value Decomposition (SVD) based algorithm is devised which combines the measurement data of the two imaging modalities in a way that is optimal in the sense of maximum likelihood. As a multiphysics problem formulation with applications in geophysics, the problem of tunnel detection based on EM and ER tomography is studied in this paper. To illustrate the connection between the Green's functions, the gradients and the Fisher information, two simple and generic forward models are described in detail regarding two-dimensional EM and ER tomography, respectively.

scholarly journals Effective Passive Multitarget Localization Using Maximum Likelihood

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yasir Munir ◽  
Muhammad Umar Aftab ◽  
Danish Shehzad ◽  
Ali M. Aseere ◽  
Habib Shah
Keyword(s):  
Computational Complexity ◽  
Maximum Likelihood ◽  
Data Fusion ◽  
Prior Knowledge ◽  
Experimental Testing ◽  
Measurement Data ◽  
Grid Search ◽  
Multiple Targets ◽  
Time Difference Of Arrival ◽  
Simulation Results

Localization of multiple targets is a challenging task due to immense complexity regarding data fusion received at the sensors. In this context, we propose an algorithm to solve the problem for an unknown number of emitters without prior knowledge to address the data fusion problem. The proposed technique combines the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurement data fusion which further uses the maximum likelihood of the measurements received at each sensor of the surveillance region. The measurement grids of the sensors are used to perform data association. The simulation results show that the proposed algorithm outperforms the multipass grid search and further effectively eliminated the ghost targets created due to the fusion of measurements received at each sensor. Moreover, the proposed algorithm reduces the computational complexity compared to other existing algorithms as it does not use repeated steps for convergence or any biological evolutions. Furthermore, the experimental testing of the proposed technique was executed successfully for tracking multiple targets in different scenarios passively.

scholarly journals Maximum likelihood linear programming data fusion for speaker recognition

2009 ◽  
Vol 51 (9) ◽  
pp. 820-830 ◽  
Author(s):  
Enric Monte-Moreno ◽  
Mohamed Chetouani ◽  
Marcos Faundez-Zanuy ◽  
Jordi Sole-Casals
Keyword(s):  
Linear Programming ◽  
Maximum Likelihood ◽  
Data Fusion ◽  
Speaker Recognition

scholarly journals Fisher information for inverse problems and trace class operators

2012 ◽  
Vol 53 (12) ◽  
pp. 123503 ◽  
Author(s):  
S. Nordebo ◽  
M. Gustafsson ◽  
A. Khrennikov ◽  
B. Nilsson ◽  
J. Toft
Keyword(s):  
Inverse Problems ◽  
Fisher Information ◽  
Trace Class ◽  
Trace Class Operators

scholarly journals A modified truncation singular value decomposition method for solving ill-posed problems

2018 ◽  
Vol 13 ◽  
pp. 174830181881360 ◽  
Author(s):  
Zhenyu Zhao ◽  
Riguang Lin ◽  
Zehong Meng ◽  
Guoqiang He ◽  
Lei You ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Decomposition Method ◽  
Singular Value ◽  
Numerical Results ◽  
New Method ◽  
Truncated Singular Value Decomposition ◽  
Singular Value Decomposition Method ◽  
Ill Posed ◽  
Value Decomposition

A modified truncated singular value decomposition method for solving ill-posed problems is presented in this paper, in which the solution has a slightly different form. Both theoretical and numerical results show that the limitations of the classical TSVD method have been overcome by the new method and very few additive computations are needed.

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