A survey of automatic target recognition technology based on multi-source data fusion

With the rapid advancement of big data, the Internet of Things, and artificial intelligence, informatization, and intelligent warfare have become central themes in modern conflict. Warfare has evolved from traditional methods to unmanned and intelligent operations, such as swarming and wolf-pack tactics, presenting significant challenges for the development of new-era combat systems and intelligent unmanned platforms. Military ground unmanned platforms leverage their scalability to incorporate various measuring instruments and sensors, thereby broadening their application to meet diverse combat requirements. Currently, the most prevalent and significant application of these platforms is in the reconnaissance and identification of targets, systematic threat evaluation, casualty avoidance, and enhancement of combat personnel's battlefield survival rates. Consequently, both domestic and international scholars have increasingly focused on developing various Automatic Target Recognition (ATR) technologies, with extensive research dedicated to ATR based on multi-source data fusion. To elucidate the current state of research, this paper provides a detailed review of various multi-source data fusion algorithms and automatic target recognition algorithms, offering insights and future research directions.