Effective Passive Multitarget Localization Using Maximum Likelihood
| Autor: | Danish Shehzad, Ali M. Aseere, Habib Shah, Muhammad Umar Aftab, Yasir Munir |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Computational complexity theory Article Subject Computer Networks and Communications Computer science Context (language use) TK5101-6720 Sensor fusion Tracking (particle physics) Multilateration Convergence (routing) Hyperparameter optimization FDOA Telecommunication Electrical and Electronic Engineering Algorithm Information Systems |
| Zdroj: | Wireless Communications and Mobile Computing, Vol 2021 (2021) |
| ISSN: | 1530-8669 |
| DOI: | 10.1155/2021/6567346 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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