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

机译：实时传感器管理和航空摄影机控制应用

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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：摄像机系统越来越多地用于载人和无人飞机的情报，监视和侦察（ISR）任务中。对于载人飞行任务，通过平移-倾斜-缩放（PTZ）命令自动控制摄像机的视野和方向，可以释放飞行员执行更高的认知功能的能力。对于无人任务，自动PTZ命令可实现更高的自治度。本文解决了控制安装在飞行器上的摄像头的视野和方向的问题，以使命令的关注区域（AOI）生成的图像具有指定的地面采样距离（GSD）。这个问题由于以下几个因素而变得复杂：首先，虽然假定车辆的瞬时位置已知，但事先不知道其路线（包括高度分布图），因此无法进行预先规划。其次，AOI的大小是任意的，这意味着相机的单个外观可能不足以覆盖它们。第三，摄像机的转换时间可能太慢，并且在更改PTZ参数时摄像机可能无法运行。最后，在给定AOI的情况下，可以实现所需GSD的时间间隔因不同的相机而异。在本文中，我们首先证明成功获取AOI图像（即使用规定的GSD进行收集）只有在飞机位于AOI球形盖内时才可能完成，具体取决于AOI的位置和所需的GSD。给定一连串请求的AOI，并带有相应的GSD和值来表征其对用户的重要性，然后，我们将开发算法，以在满足请求的GSD的同时，为最有价值的请求集合提供服务。开发了三种算法：（1）一种调度算法，用于确定在给定时刻服务于哪个请求;（2）一种准入控制算法，在系统过载（请求多于可用时间轴）的情况下，能够进行适度降级;以及（3）一种算法，用于确定哪个平台（空中交通工具和摄像机）为给定请求提供服务。我们依次检查这三种算法。调度算法以实时系统文献中最早的截止日期优先（EDF）方案为中心。通过计算外推的飞机轨迹与先前定义的球冠之间的交点，可以确定服务AOI请求的期限。基于对EDF的可调度性测试，开发了准入控制算法。最后，在平台之间路由请求的算法基于车间调度问题，从根本上促进了平台之间的负载平衡。我们介绍了一种启发式方法，称为“最小-最大利用率”，其启发是针对针对机修车间调度问题而开发的最小化制造期过程。给出了广泛的仿真结果，说明了与其他替代方法相比，该算法的优越性能。

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