Cognitive FDA Radar With Fuzzy Entropy-Driven Joint Beam-Phase Optimization for Active Antijamming

This paper proposes a fuzzy entropy-driven adaptive joint beam and phase regulation method for cognitive frequency diverse array (CFDA) radar to achieve sustained active anti-jamming capability during normal operation. Based on models including the generalized frequency diverse array (GFDA) architecture, beam collective efficiency (BCE), peak sidelobe level (PSLL), and phase center, this research addresses the fundamental issue of inconsistent quantification standards between beam energy and phase distribution metrics by extending the traditional fuzzy entropy concept into a three-dimensional continuous domain, thereby establishing a unified mathematical foundation for joint optimization. The proposed CFDA system adopts an adaptive joint beam-phase regulation strategy that simultaneously optimizes array configuration parameters and frequency offset distributions, enabling the radar to maintain active anti-jamming capability while adaptively tracking dynamic targets. This strategy achieves synergistic optimization of beam regulation and phase regulation through joint optimization of array configuration and frequency offset distribution. Comprehensive performance evaluation through numerical simulations validates the method's excellent performance in simultaneously achieving target tracking and jamming suppression in complex electromagnetic environments.