Bernadus H. Sirenden, Yanti Susanti, Ibnu Susanto Joyosemito, Dinar Nurcahyono, Rodiah Nurbaya Sari, Riyanto, Joni, Sabar, Yusuf Affandi, Arief Wicaksono
Based on a systematic literature review (SLR) of 72 primary studies, this research highlights the significant potential of Field-Programmable Gate Arrays (FPGAs) in enhancing navigation systems for Autonomous Underwater Vehicles (AUVs) operating in GPS-denied environments. FPGAs offer key advantages such as low-latency parallel processing, energy-efficient operation, and hardware adaptability, all of which are critical for long-duration missions involving high-frequency sensor data acquisition. However, a notable research gap exists, as most previous studies have focused on algorithmic aspects or general embedded systems without considering FPGA-specific implementations. This review identifies that in the context of FPGA-based AUVs, acoustic (M3, M4) and visual (M2) navigation methods are the most commonly used, while inertial (M1) and hybrid (M12) navigation, although more prevalent in the broader literature, are still underexplored on FPGA platforms. For sensor fusion, the Kalman filter (E1) is the most widely applied method, yet its FPGA implementations for AUVs remain very limited. Particle filters (E2) and machine learning-based approaches (E7) emerge as strong alternatives that are well suited to the parallel capabilities of FPGAs. Despite their promise, FPGA implementations face challenges such as design complexity, resource consumption, and difficulties in fully parallelizing certain algorithms, which warrant further attention to unlock their full potential in next-generation AUV navigation. © 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Research Centre for Electronics, BRIN, Indonesia; Master of Science in Sustainability, Pertamina University, Indonesia; Research Center for Testing and Standards Technology, BRIN, Indonesia; Research Center for Industrial Systems and Sustainable Manufacturing, BRIN, Indonesia; Instrumentation and Automation Engineering Department, Faculty of Industrial Technology, Institut Teknologi Sumatera, South Lampung, Indonesia; Agricultural Water Management Departement, Agricultural Engineering Polytechnic, Indonesia