Tracing the evolution: a review of dictionary-learning in ATR systems

Abstract

This work provides a detailed survey of the progress in dictionary-learning methods in the area of Automatic Target Recognition (ATR) systems, emphasizing the importance these techniques, in general, and the role of the methodologies in respect of growing accuracy and the efficiency, in particular, play in the tasks of identifying and classifying targets. Using an approach that combines literature review and bibliometric analysis, we unravel the history of dictionary learning in ATR, pointing out its interaction with machine learning algorithms, sparse representation, and radar imaging technologies. The core themes and innovations that shape the field are identified in the analysis aided by VOSviewer, with the integration of radar imaging and machine learning coming out as vital for developing efficient target recognition strategies. This study reveals a major trend of utilizing advanced computational models in dealing with complexities of modern surveillance and reconnaissance missions that helps increase operational efficiency in military as well as civilian applications. This review not only provides an overview of the current state of ATR research but also identifies potential developing lines, highlighting the crucial role of continuous improvement of computational algorithms and the interrelation between signal processing and machine learning for realization of unparalleled accuracy and efficiency in ATR systems.