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| A Survey of Task-Driven Intelligent Target Recognition Methods in Complex Battlefield Environments |
| LUO Zhijun1， WANG Jianrui2，3， YIN Jiawei2，3 |
| 1. Shanghai Academy of Spaceflight Technology， Shanghai 201109， China；
2. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109， China；
3. National Key Laboratory of Automatic Target Recognition （Shanghai）， Shanghai 201109， China |
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Abstract Complex battlefield environments are characterised by diverse target types, intricate task constraints, and highly dynamic environmental conditions, thereby imposing requirements on intelligent target recognition that go beyond conventional optimisation of perceptual accuracy. In these environments, recognition results are not only used to describe target attributes but also directly affect the reliability of task planning and decision-making. However, most current target recognition research mainly concentrates on static scenarios and perception-based metrics, which do not adequately capture the practical significance of recognition results in task execution. To address this gap, a task-driven paradigm for target recognition has gradually emerged in recent years, in which task-related information is explicitly incorporated into model design, training, and evaluation, thereby enabling recognition results to support task deployment and system-level decision-making better. Following this research trend, this paper presents a systematic survey of task-driven intelligent target recognition methods from a methodological perspective. Firstly, the fundamental concepts of task-driven target recognition were analysed, and its key differences from traditional perception-driven approaches were clarified with respect to output representations, optimisation objectives, and system role positioning. Then, from the perspective of task-related information modelling, existing methods were systematically reviewed with respect to semantic and attribute representations, target state and behaviour modelling, and uncertainty and risk representation. After that, task-constraint modelling during training and optimisation, as well as the collaborative interfaces between recognition outputs and task-execution and decision modules, were further discussed. Finally, using the typical demands of complex battlefield environments as a key context, the paper summarized the major challenges in task-driven target recognition, including adapting to dynamic environments, managing unknown targets, ensuring trustworthy uncertainty representation, and coordinating at the system level. It also outlines potential directions for future research.
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Received: 09 January 2026
Published: 11 March 2026
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2026, Vol. 9 
