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摘要: 该文面向高动态强对抗战场环境下复杂感知系统在探测、跟踪及抗干扰等核心任务中的适应性难题，提出以信息驱动为核心的系统理论模型与构建方法体系。具体而言，构建基于语法、语义、语用的多层信息描述框架突破单一语法结构及表层语义的局限，提出系统动态演化架构打破传统静态建模与固定模式设计的应用壁垒。此外，将上述理论成果应用于分布式雷达探测系统实践，针对系统寻优的复杂性难题，设计基于有限场景交互学习机制与结构化分层优化算法，实现系统有序组织与能力涌现，为复杂战场环境下智能感知系统设计提供了理论范式与技术路径。Abstract: 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.
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图 1 复杂感知系统语法、语义及语用信息图示
Figure 1. Illustration of syntactic, semantic and pragmatic information for complex perception systems
图 2 任务驱动分布式探测系统与信息驱动分布式探测系统区别
Figure 2. Differences between task-driven distributed detection systems and information-driven distributed detection systems
图 3 系统策略空间优化与场景策略空间精简
Figure 3. Optimization of system strategy space and simplification of scenario strategy space
图 4 分布式雷达探测系统演化框架
Figure 4. Evolutionary framework of distributed radar detection systems
图 5 干扰类型判别及关键参数估计实验效果
Figure 5. Experimental effect of radar interference signal type discrimination and key parameter estimation
图 6 协同抗干扰网络架构及实验效果
Figure 6. Network architecture of cooperative anti-jamming for radar systems and its experimental effect
1 分布式雷达探测系统分层优化算法
1. Hierarchical optimization algorithm for distributed radar detection systems
初始化：节点信息协同参数$\alpha^{(0)} $，系统自组织策略参数$\beta^{(1)} $ 配置：外层迭代次数T，内层迭代次数K，节点N 1. for 外层迭代t=1 to T–1 do 2. // 内层优化：节点协同参数 3. 利用系统协同自组织网络收集多N个探测节点的接收信息
$\mathcal{R}_{t}=F_{{\mathrm{o}}}\left(R_{1}^{t},R_{2}^{t}, \cdots, R_{N}^{t} ; \beta^{t}\right) $4. $\alpha^{(t, 0)} \leftarrow \alpha^{(t-1)} $, 5. for内层迭代k=1 to K–1 do 6. 节点信息协同认知处理网络，进行多节点信息的解纠
缠、去冗余和信息融合，完成环境认知7. 计算感知表征：$Y \leftarrow F_{{\mathrm{c}}}\left(\mathcal{R}_{t} ; \alpha^{(t, k-1)}\right) $ 8. 互信息估计：计算物理空间X与感知空间Y的互信息： $\tilde{I}_{\alpha^{(t, k-1)},{\beta}^{(t)}}(X ; Y) $ 9. 更新$\alpha $参数： $\alpha^{(t, k)} \leftarrow \alpha^{(t, k-1)}-\eta_{\alpha} \nabla_{\alpha} \tilde{I} $ 10. end for 11. $\alpha^{(t)} \leftarrow \alpha^{(t, K)} $ 12. // 外层优化：系统策略参数 13. 计算感知表征：$Y \leftarrow F_{{\mathrm{c}}}\left(\mathcal{R}_{t} ; \alpha^{(t)}\right) $ 14. 互信息估计：计算物理空间X与感知空间Y的互信息： $\tilde{I}_{\alpha^{(t)}, \beta^{(t)}}(X ; Y) $ 15. 反向更新$\beta $参数： $\beta^{(t+1)} \leftarrow \beta^{(t)}-\eta_{\beta} \nabla_{\beta} \tilde{I} $ 16. end for 17. return 最优参数$\alpha^{*} \leftarrow \alpha^{(T, K)}, \beta^{*} \leftarrow \beta^{(T)} $ 
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