Passive Target Motion Analysis by Fusion of Linear Arrays and Sonobuoys in a Cluttered Environment
| Autor: | Antoine Lebon, Claude Jauffre, Annie-Claude Perez, Jeremy Payan, Dann Laneuville |
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| Přispěvatelé: | Institut des Matériaux, de Microélectronique et des Nanosciences de Provence (IM2NP), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Naval Group |
| Rok vydání: | 2021 |
| Předmět: |
time difference of arrival (TDOA)
Fusion observability business.industry Computer science Fisher information matrix probabilistic data association with maximum likelihood estimation (ML-PDA) sonobuoy Aerospace Engineering target motion analysis Target Motion Analysis vertical array Linear arrays Computer vision multipath Artificial intelligence Electrical and Electronic Engineering business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | IEEE Transactions on Aerospace and Electronic Systems IEEE Transactions on Aerospace and Electronic Systems, 2021, 57 (6), pp.3941-3951. ⟨10.1109/TAES.2021.3082667⟩ |
| ISSN: | 2371-9877 0018-9251 |
| DOI: | 10.1109/taes.2021.3082667 |
| Popis: | International audience; This paper is devoted to the analysis of the motion of a target using information from two kinds of cooperative maritime sensors: A wireless network of sonobuoys detecting a signal emitted by a source in motion with constant velocity; Vertical antennas measuring the cosine of the elevation angles of the received signal. We prove that the trajectory of the source is observable, under non-restrictive assumptions concerning the scenario. After thresholding, the data are surrounded by false alarms; therefore, a probabilistic data association model is employed. The joint exploitation of the measurements of the time differences of arrival together with the measurements of the cosines of the elevation angles allow estimating the trajectory of the source. The empirical performance of the maximum likelihood estimator (MLE), evaluated by extensive simulations, reaches the asymptotic performance given by the Cramér-Rao lower bound. Finally, we extend our study to the case where the energy of the data is available, after thresholding. Again, the MLE is efficient and its performance is significantly improved. |
| Databáze: | OpenAIRE |
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