针对空间上分散部署的多雷达,在目标多元、动态变化的博弈对抗场景下,多雷达协同处理方式与目标/ 环境/ 资源动态自适应匹配的问题,设计了一种多雷达多层级自适应协同处理框架,包括信号级、特征级、点迹级和航迹级等4 个层级。以工程化应用为目标,建立了基于统一空间栅格的多雷达信号级联合检测模型,以时空配准、融合多雷达信号。设计了基于特征级的分布式检测模型,以压缩传输数据量,实现融合检测。综合分析了不同层级协同处理技术的费效比,给出了典型应用场景下协同处理方式的自适应选择原则。通过仿真实验和典型试验系统,对文中提出技术的有效性进行验证,结果表明,该技术可以动态适应目标、环境的变化,提升多雷达协同探测的效能。

In adversarial scenario with multiple and dynamic targets, to match cooperative processing methods with the targets, environment and the resources of distributed multi-radar, a multi-level cooperative processing framework was designed. The framework includes four cooperative processing levels: signal level, feature level, point level and track level. Aiming at application, a multi-radar signal level joint detection model based on unified spatial grid was established. Time-space registration and multi-radar signal fusion were achieved by the model. A feature level distributed detection model was designed. It compressed transmission data for fusion detection. The cost-effectiveness of cooperative processing technique at different levels were comprehensive analysed. Adaptive selection principles for cooperative processing methods in typical application scenarios were provided. The effectiveness of the proposed technique was confirmed through simulation and typical experiment system. Results indicates that the technique can dynamically adapt to changes in targets and environments, also improve the efficiency of multi-radar cooperative detection.

TN957