Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements and Challenges

TL;DR

This paper reviews recent advances in applying deep learning to radar data analysis, highlighting improvements over traditional methods, while discussing current challenges in stability and generalization.

Contribution

It provides a comprehensive overview of recent deep learning applications in radar data analysis, emphasizing achievements, advancements, and ongoing challenges.

Findings

Deep learning improves radar data analysis performance.

Challenges include stability and generalization issues.

The paper discusses future potentials in the field.

Abstract

Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this paper, recent relevant scientific investigation and practical efforts using Deep Learning (DL) models for weather radar data analysis and pattern recognition have been reviewed; particularly, in the fields of beam blockage correction, radar echo extrapolation, and precipitation nowcast. Compared to traditional approaches, present DL methods depict better performance and convenience but suffer from stability and generalization. In addition to recent achievements, the latest advancements and existing challenges are also presented and discussed in this paper, trying to lead to reasonable potentials and trends in this highly-concerned field.