This study addresses the challenge of anticipatory resource allocation in field-deployed communication networks under dynamic unmanned aerial vehicle jamming. In such scenarios, energy supply is severely constrained. It cannot be replenished in real time, necessitating a one-time resource pre-allocation that must remain effective throughout the mission. To overcome these limitations, we propose a novel optimization framework consisting of four integrated components: (1) independent threat assessment via trajectory-coverage spatial mapping using digital elevation models and ray-tracing algorithms, (2) evidence-theoretic fusion of heterogeneous information sources—including objective intelligence data and subjective expert knowledge, (3) jamming distribution modeling through dedicated probability transformation algorithms for fixed-interval and continuous random jamming modes, and (4) decoupled resource-confidence optimization solved via convex programming. By employing evidence discount factors and Dempster’s combination rule, the framework quantifies reliability disparities. It integrates multiple heterogeneous sources and uses theoretically derived, forward-computable models—combining Binomial distributions, piecewise cubic Hermite interpolation, and uniform distribution assumptions—to efficiently convert threat basic probability assignments into jamming duration probability density functions. Extensive Monte Carlo simulations demonstrate significant improvement in mission assurance metrics, with consistent performance under diverse uncertainties. The approach is also validated in cross-domain applications using Bohai rescue data, confirming its utility in resource-limited, highly uncertain environments.
Yu et al. (Mon,) studied this question.
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