Enhancing radar signal processing through LVQ-Kalman fusion: a tsunami prediction perspective

Abstract

In radar signal processing, the pursuit of precise prediction algorithms motivates the exploration of innovative methodologies. This study introduces a pioneering fusion of learning vector quantization (LVQ)- Kalman, merging LVQ with the advanced Kalman filter. The primary aim is to enhance adaptability and robustness, vital in weather monitoring and military surveillance. LVQ, known for its efficacy in pattern recognition and prediction, adjusts prototype vectors iteratively based on input data, ideal for radar signal intricacies. Various LVQ types are incorporated, tailored meticulously for specific radar applications. The Kalman filter, originally for aerospace, excels in tracking and predicting dynamic systems, seamlessly integrated to address uncertainties in radar data. By combining LVQ’s pattern recognition with the Kalman filter’s adaptability, the fusion aims to create a versatile system navigating radar data intricacies. Applications range from airborne target tracking to weather analysis and military surveillance. The integrated approach offers adaptability and robustness, vital for real-world implementations, particularly in tsunami detection. Future research may explore deep learning to further enhance adaptability. This fusion technique presents significant potential for advancing radar signal processing, promising accurate and adaptive systems, especially in critical applications like tsunami detection.