Abstract
The Indian Regional Navigation Satellite System (IRNSS), or NavIC, provides regional satellite positioning services across India and parts of Southeast Asia. In this study, we develop and evaluate a software-defined receiver (SDR) enhanced with deep learning techniques to acquire the IRNSS Standard Positioning Service (SPS) L5-band signal. The SDR architecture incorporates data-driven improvements in acquisition decision-making while retaining compatibility with the IRNSS signal structure as specified in the official ICD. Field experiments were conducted in Hanoi, Vietnam, a location situated at the fringe of NavIC’s primary service area. Signal data were collected using a low-cost RF front-end connected to a rooftop-mounted antenna. Experimental results demonstrate that the proposed SDR is capable of reliably acquiring and tracking up to four IRNSS satellites under nominal conditions. The average C/N0 ranged from 30 to 42 dB-Hz, and successful position solutions were obtained with a horizontal accuracy of approximately 25 meters. Additionally, the deep learning-based acquisition module improved robustness in low-SNR scenarios. This work represents the first implementation of a learning-aided IRNSS receiver validated in Vietnam and offers insights into extending NavIC-based positioning services to broader Southeast Asian regions.