@inproceedings{14df3ddbf79b4b13a730d67c887aa6d7,
title = "Advanced Radar Target Tracking: Synergizing Deep Learning LSTM Clutter Filtering with Joint Probabilistic Data Association",
abstract = "This paper introduces an innovative approach for handling radar data that combines Long Short-Term Memory (LSTM) networks with Joint Probabilistic Data Association (JPDA). To tackle the challenge of clutter in radar signal processing we utilized the sequential data analysis capabilities of LSTM to minimize clutter and enhance measurements quality effectively. Subsequently, JPDA is employed for precise target tracking data association. This collaborative approach results in enhancements of tracking accuracy, completeness, and ambiguity which underscores the approach{\textquoteright}s ability to improve radar multi-target tracking performance. This paper thoroughly assesses the proposed approach, backed up by experimental data and comparative analysis.",
keywords = "Data association, Deep learning, LSTM, Multi-target tracking, Radar tracking",
author = "Esra Alhadhrami and Cl{\'e}ment Pira and Rami Kassab and Segrouchni, \{Amal El Fallah\} and Frederic Barbaresco and Alhammadi, \{Ahmed Y.\} and Yeun, \{Chan Yeob\} and Deepak Puthal",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 7th International Conference on Data Analytics and Cyber Security, DACS 2024 ; Conference date: 20-12-2024 Through 22-12-2024",
year = "2026",
doi = "10.1007/978-981-95-2680-2\_46",
language = "British English",
isbn = "9789819526796",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "593--607",
editor = "Deepak Puthal and Panigrahi, \{Bijaya Ketan\} and Niranjan Ray and Zhiguo Ding",
booktitle = "Synergies in Data Analytics and Cyber Security - Proceedings of the International Conference, DACS 2024",
address = "Germany",
}