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Collaborative Unmanned Aerial Systems for Effective and Efficient Airborne Surveillance

机译：用于有效和高效的空中监测的协同无人空中系统

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Unmanned aerial vehicles (UAVs), commonly known as drones, have the potential to enable a wide variety of beneficial applications in areas such as monitoring and inspection of physical infrastructure, smart emergency/disaster response, agriculture support, and observation and study of weather phenomena including severe storms, among others. However, the increasing deployment of amateur UAVs (AUAVs) places the public safety at risk. A promising solution is to deploy surveillance UAVs (SUAVs) for the detection, localization, tracking, jamming and hunting of AUAVs. Accurate localization and tracking of AUAV is the key to the success of AUAV surveillance. In this article, we propose a novel framework for accurate localization and tracking of AUAV enabled by cooperating SUAVs. At the heart of the framework is a localization algorithm called cooperation coordinate separation interactive multiple model extended Kalman filter (CoCS-IMMEKF). This algorithm simplifies the set of multiple models and eliminates the model competition of each motion direction by coordinate separation. At the same time, this algorithm leverages the advantages of fusing inulti-SUAV cooperative detection to improve the algorithm accuracy. Compared with the classical interacting multiple model unscented Kalman filter (IMM-UKF) algorithm, this algorithm achieves better target estimation accuracy and higher computational efficiency, and enables good adaptability in SUAV system target localization and tracking.
机译：无人驾驶飞行器（无人机），通常称为无人机，有可能在监测和检查物理基础设施，智能急诊/灾害响应，农业支持以及天气现象观察和观察和研究等领域中实现各种各样的有益应用包括严重风暴等。然而，越来越多的业余无人机（Auavs）部署（Auavs）将公共安全置于风险之中。一个有希望的解决方案是部署监控无人机（Suavs），了解Auavs的检测，本地化，跟踪，干扰和狩猎。 Auav的准确本地化和跟踪是Auav监控成功的关键。在本文中，我们提出了一种新颖的框架，以准确定位和跟踪通过合作苏瓦公司启用的Auav。框架的核心是一种称为合作坐标分离交互式多模型扩展卡尔曼滤波器（COCS-IMMEKF）的本地化算法。该算法简化了多个模型集，并通过坐标分离消除了每个运动方向的模型竞争。同时，该算法利用了融合了inulti-uivi-suav协作检测来提高算法精度的优点。与经典交互多模型无创的卡尔曼滤波器（IMM-UKF）算法相比，该算法实现了更好的目标估计精度和更高的计算效率，并实现了苏瓦系统目标本地化和跟踪的良好适应性。

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