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Riyanto, Eko
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Journal of Technology Informatics and Engineering

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AI-Driven Adaptive Radar Systems for Real-Time Target Tracking in Urban Environments Ghofur, Muhammad Jamal Udin; Riyanto, Eko
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i1.289

Abstract

Radar systems play a crucial role in target tracking within urban environments, where challenges such as clutter, multipath effects, and electromagnetic interference significantly impact detection accuracy. Traditional radar methods often struggle to adapt to dynamic urban conditions, leading to decreased reliability in real-time target tracking. This study aims to develop and evaluate an AI-driven adaptive radar system that enhances tracking accuracy in urban settings. The research employs a quantitative approach using simulations to model radar signal processing under various environmental conditions. The AI model, based on Convolutional Neural Networks (CNN), is trained to optimize radar performance by filtering out noise and dynamically adjusting detection parameters. The results indicate that the AI-based radar system achieves a tracking accuracy of 95.2%, significantly outperforming traditional radar systems, which only reach 80% accuracy. Additionally, the AI-enhanced radar reduces response time to 120 milliseconds, compared to 250 milliseconds in conventional systems, demonstrating improved real-time processing capabilities. The system also exhibits greater resilience to high-clutter environments, maintaining stable target detection despite signal interference. These findings highlight the potential of AI in enhancing radar functionality for applications such as surveillance, traffic monitoring, and security. Future research should focus on integrating AI-driven radar with real-world radar hardware, exploring multi-sensor fusion, and refining adaptive learning techniques to further optimize tracking performance in complex environments
Co-Authors agung nusantoro agung setiawan Agus Imam Santoso AGUS PURWANTO Akhmad Alim, Akhmad Efendi, Ahmad Bahtiar Endin Mujahidin Ghofur, Muhammad Jamal Udin Hartono, Lia Vita Ilmi Putri Heni Pujiastuti Hirmawan, Yuda Mahmudi Mahmudi Mashuri, Agus Alwi Milyuantara, Rizal Nesia Andriana Pusparini, Larasati Safitri, Rismawati Nur Septia Lutfi Solikhin Solikhin Tamam, Abas Mansur Tiffany, Yipson Winarsih Winarsih