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Sensor management in real time and a camera control application for air vehicles

机译：传感器管理实时和用于空气车辆的相机控制应用

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Camera systems are increasingly utilized in Intelligence, Surveillance and Reconnaissance (ISR) missions on manned and unmanned aircraft. For manned missions, the automatic control of the camera field of view and orientation through pan-tilt-zoom (PTZ) commands frees the pilot to perform higher cognitive functions. For unmanned missions, automatic PTZ commands enable higher degrees of autonomy. This paper addresses the problem of controlling the field of view and orientation of cameras mounted on air vehicles so that the resulting images of commanded areas of interest (AOIs) have specified ground sampling distances (GSDs). The problem is complicated by several factors: First, while the instantaneous positions of the vehicles are assumed to be known, their routes, including their altitude profiles, are not known in advance, which precludes preplanning. Second, the sizes of the AOIs are arbitrary, implying that a single look of the camera may not be sufficient to cover them. Third, the slew times of the cameras may be too slow, and the cameras may not operate while PTZ parameters are changing. Finally, given an AOI, the time intervals when the desired GSDs are achievable varies for different cameras. In the paper, we first show that successful collection of an AOI image (i.e. collection with a prescribed GSD) is only possible if the aircraft lies inside a spherical cap depending on the position of the AOI, and the desired GSD. Given a stream of requested AOIs, with corresponding GSDs and values characterizing their importance to a user, we then develop algorithms to service the most valuable ensemble of requests, while satisfying the requested GSDs. Three algorithms are developed: (1) a scheduling algorithm, for deciding which request to service at a given instant of time, (2) an admission control algorithm, enabling a graceful degradation in case of system overload (more requests than available timeline) and (3) an algorithm for deciding which of the platforms (air - ehicle and camera) services a given request. We examine each of the three algorithms in turn. The scheduling algorithm is centered on the earliest deadline first (EDF) scheme from the real-time systems literature. Deadlines for servicing AOI requests are determined by computing the intersection between the extrapolated aircraft trajectory and the spherical caps defined earlier. The admission control algorithm is developed based on schedulability tests for EDF. Finally, the algorithm for routing the requests among platforms are based on machine shop scheduling problems, which essentially promote load balancing among the platforms. We introduce a heuristic called min-max utilization, inspired on the makespan minimization procedures developed for machine shop scheduling problems. Extensive simulation results are presented, illustrating the superior performance of the algorithms in contrast to other alternatives.
机译：相机系统越来越多地用于载人和无人驾驶飞机上的智力，监视和侦察（ISR）特派团。对于载人任务，通过Pan-Tilt-Zoom（PTZ）命令的自动控制视图和方向的方向释放飞行员以执行更高的认知功能。对于无人任务，自动PTZ命令可以实现更高程度的自主权。本文解决了控制空气车上安装在空气车上的摄像机视野和方向的问题，以便所得到的感兴趣区域（AOIS）的图像具有特定的地面采样距离（GSD）。问题是若干因素的复杂性：首先，虽然假设车辆的瞬时位置是已知的，但是它们的路线（包括其高度轮廓）预先知道，这排除了预先预期。其次，Aois的尺寸是任意的，这意味着相机的单个外观可能不足以覆盖它们。第三，相机的转换时间可能太慢，并且在PTZ参数正在发生变化时，相机可能无法操作。最后，给定AOI，当所需的GSD可以实现时的时间间隔因不同的摄像机而变化。在论文中，我们首先显示AOI图像的成功收集（即用规定的GSD收集），如果飞机在球形帽内部沿着AOI的位置和所需的GSD呈现。鉴于请求的AOIS流，具有对应的GSD和值表征对用户的重要性，然后我们开发算法以提供最有价值的请求集合，同时满足所请求的GSD。开发了三种算法：（1）调度算法，用于在给定的时间瞬间决定哪个请求，（2）一个准入控制算法，在系统过载（比可用时间轴的请求提供更多的请求）和（3）用于决定哪个平台（空中 - ehicle和相机）服务给定请求的算法。我们依次检查三种算法中的每一个。调度算法以实时系统文献的最早截止日期（EDF）方案为中心。通过计算外推的飞机轨迹与前面定义的球形帽之间的交叉来确定用于维修AOI请求的截止日期。基于EDF的调度性测试开发了准入控制算法。最后，用于路由平台之间请求的算法基于机器店调度问题，从而大大促进了平台之间的负载平衡。我们介绍了一个称为Min-Max利用的启发式，启发了用于为机器商店调度问题开发的Mapespan最小化程序。提出了广泛的仿真结果，说明了与其他替代方案相比的算法的优异性能。

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