Effective Passive Multitarget Localization Using Maximum Likelihood

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 tec...

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Veröffentlicht in:	Wireless communications and mobile computing Jg. 2021; H. 1
Hauptverfasser:	Munir, Yasir, Aftab, Muhammad Umar, Shehzad, Danish, Aseere, Ali M., Shah, Habib
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Hindawi 2021 John Wiley & Sons, Inc
Schlagworte:	Accuracy Algorithms Complexity Data integration Emitters Genetic algorithms Geometry Localization Multiple target tracking Multisensor fusion Noise Optimization Population Sensors Standard deviation
ISSN:	1530-8669, 1530-8677
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Abstract	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.
AbstractList	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.
Author	Munir, Yasir Shehzad, Danish Aseere, Ali M. Shah, Habib Aftab, Muhammad Umar
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Copyright	Copyright © 2021 Yasir Munir et al. Copyright © 2021 Yasir Munir et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Snippet	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...
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SubjectTerms	Accuracy Algorithms Complexity Data integration Emitters Genetic algorithms Geometry Localization Multiple target tracking Multisensor fusion Noise Optimization Population Sensors Standard deviation
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Title	Effective Passive Multitarget Localization Using Maximum Likelihood
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