Optimization and Machine Learning Algorithms for Intelligent Microwave Sensing: A Review

Microwave sensors find growing applications in remote sensing, material analysis, and process monitoring. Yet, the intricate interplay between microwaves and materials poses challenges in data analysis. This chapter offers an insightful exploration of microwave sensors, delving into the complexities of data analysis and proposing the utilization of artificial neural networks for effective and resilient processing. The objective is to establish a comprehensive foundation for advancing research and developing practical neural network-driven solutions for real-world microwave sensing and measurement. Also, we delve into the optimization of a microwave sensor employing various techniques. The primary objective is to refine the sensor’s parameters to attain optimal performance in terms of sensitivity and accuracy. The investigation encompasses a thorough analysis of the sensor’s behavior across diverse conditions. The synergy of multiple optimization techniques is considered to provide a comprehensive evaluation of their impact on sensor performance. This chapter not only advances our understanding of microwave sensor optimization but also offers a versatile framework for leveraging different optimization techniques to enhance sensing capabilities in diverse applications.