RL-Driven Bandwidth Adaptation for Cognitive Weather Radars

Abstract

The problem of enabling adaptive capabilities in the context of weather radar is considered in this paper. Inspired by the cognitive radar framework, an approach based on Reinforcement Learning (RL) is formulated to deal with the monitoring of multiple storm cells moving near a potential area of interest. The approach aims to dynamically adjust the radar waveform bandwidth, and consequently maximum measurable range and range resolution, in order to provide the best monitoring based on a purposely-defined reward function. The approach is successfully validated with a simulator developed in Python & StoneSoup. Results demonstrate that the proposed method outperforms traditional fixed-scan ('sit and spin') strategies commonly used in weather radar operations.