基于粒子群算法的“干中探”一体化信号设计

    Integrated Signal Design for Detection in Jamming Based on Particle Swarm Optimization

    • 摘要: 当前作战飞机及其航空电子系统(/任务系统)已经进入跨代新时代,无论是有人机还是无人机,战斗机还是轰炸机,无一例外都是高度隐身,作战任务和飞机平台必然对任务系统的SWAP(尺寸、重量、功耗和价格)提出跨代要求,各功能子系统从天线孔径开始必然从四代机的高度综合化走向下一代的一体化,其中雷达、电子战和CNI(通信/导航/识别)共享资源乃至共享信号成为必然,以此为背景,作为前期技术探索,本文提出了基于粒子群算法的干扰探测一体化信号设计方法。首先,构建信号探测和干扰性能定量指标,建立一体化信号优化目标函数,通过粒子群算法对优化问题进行求解。然后,为充分考虑实际复杂对抗环境,分别对敌方雷达信号在参数先验与参数估计的情况下进行讨论,并对不同情况分别采用CLEAN算法、去斜滤波及稀疏恢复算法对同频干扰进行抑制。最后,仿真分析表明,所设计信号具有优良的探测和干扰性能,论证了本文提出方法的有效性。

       

      Abstract: The current generation of combat aircraft and their avionics (mission systems) have entered a new era characterized by intergenerational advancements. Whether manned or unmanned, fighter or bomber, all platforms are inherently designed with advanced stealth capabilities. Consequently, the operational tasks and aircraft platforms inevitably impose stringent intergenerational requirements on the SWAP (Size, Weight, Power, and Price) of the mission systems. Functional subsystems, starting from the antenna aperture, must transition from the highly integrated designs seen in fourth-generation aircraft to the more unified systems of the next generation. Among these, radar, electronic warfare, and CNI (Communications, Navigation, and Identification) systems must share resources and even share signals. As part of early-stage technology exploration does this paper put forward an integrated signal design method for interference detection, utilizing Particle Swarm Optimization (PSO). First, quantitative metrics for signal detection and jamming performance are constructed, establishing an integrated signal optimization objective function, which is then solved using the Particle Swarm Optimization algorithm. Subsequently, to fully account for practical complex adversarial environments, two scenarios are examined: one with a priori knowledge of enemy radar signal parameters, and the other where parameters must be estimated. The CLEAN algorithm is applied in the first case, while dechirping filtering along with sparse recovery algorithms is adopted in the second, respectively, to suppress co-frequency interference. Finally, simulation results demonstrate that the designed signal exhibits excellent detection and jamming performance, confirming the effectiveness of the proposed method.

       

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