Information Theory and Construction Methods of Complex Perception Systems

This paper addresses the challenges of adapting complex perception systems to perform core tasks such as detection, tracking, and countermeasures in highly dynamic and adversarial battlefield environments. We propose an information-driven theoretical model and a systematic methodology for system construction. This study introduces an information-driven theoretical model and construction methodology for such systems. Specifically, a multi-layered information description framework is introduced to overcome the application barriers of traditional static modeling and fixed-pattern design. This framework is based on syntax, semantics, and pragmatics and is designed to overcome the limitations of single syntactic structures and surface-level semantics. A dynamic evolution architecture is incorporated into the system. The theoretical achievements are also applied to the practice of distributed radar detection systems. Moreover, a structured hierarchical optimization algorithm with a finite-scenario interactive learning mechanism is designed to achieve ordered system organization and capability emergence, thereby addressing the complexity of system optimization. This study provides a theoretical framework and technical approach for the design of intelligent perception systems in complex battlefield environments.