A Survey of Artificial Intelligence Approaches for Target Surveillance With Radar Sensors
Andrea Wrabel, Roland Graef, Tobias Brosch
Abstract
With the rising popularity of artificial intelligence (AI), also target surveillance based on radar sensors aims to tap the potential of AI enabled through today's computational capacities. Here, we present a survey of past approaches as well as recent hot topics in the area of AI approaches for target surveillance with radar sensors that reveal new potential for the development of novel approaches in research and practice. We focus on the major research streams of clutter identification, target classification, and target tracking, which are not only of great importance for an adequate operation of radar applications, but are also well suited for the use of AI. Thereby, we hope to contribute to a better understanding of how AI can be applied to assist conventional radar sensor approaches or even serve as an alternative. In addition, we give insight to our own findings in the selected areas.