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首页> 外文会议>Unmanned Systems Technology IX; Proceedings of SPIE-The International Society for Optical Engineering; vol.6561 >Simulating and Testing Autonomous Behaviour in Multiple Airborne Sensor Systems
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Simulating and Testing Autonomous Behaviour in Multiple Airborne Sensor Systems

机译：模拟和测试多个机载传感器系统中的自主行为

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The Multiple Airborne Sensor Targeting and Evaluation Rig (MASTER) is a high fidelity simulation environment in which data fusion, tracking and sensor management algorithms developed within QinetiQ Ltd. Can be demonstrated and evaluated. In this paper we report an observer trajectory planning tool that adds considerable functionality to MASTER. This planning tool can coordinate multiple sensor platforms in tracking highly manoeuvring targets. It does this by applying instantaneous thrusts to each platform, the magnitude of which is chosen to gain maximum observability of the target. We use an efficient search technique to determine the thrust that should be applied to each platform at each time step, and the planning horizon can either be one-step (greedy) or two-step. The measure of performance used in evaluating each potential sensor manoeuvre (thrust) is the posterior Cramer-Rao lower bound (PCRLB), which gives the best possible (lowest mean square error) tracking performance. We exploit a recent novel approach to approximating the PCRLB for manoeuvring target tracking (the "best-fitting Gaussian" (BFG) approach: Hernandez et al., 2005). A closed-form expression gives the BFG approximation at each sampling time. Hence, the PCRLB can be approximated with a very low computational overhead. As a result, the planning tool can be implemented as an aid to decision-making in real-time, even in this time-critical airborne domain. The functionality of MASTER enables one to access the performance of the planning tool in a range of sensor-target scenarios, enabling one to determine the minimal sensor requirement in order to satisfy mission requirements.
机译：多机载传感器瞄准和评估装备（MASTER）是一个高保真模拟环境，在其中可以演示和评估QinetiQ Ltd.开发的数据融合，跟踪和传感器管理算法。在本文中，我们报告了一种观察者轨迹计划工具，该工具为MASTER添加了相当多的功能。该计划工具可以协调多个传感器平台，以跟踪高度机动的目标。它通过向每个平台施加瞬时推力来做到这一点，选择其大小以获得目标的最大可观察性。我们使用有效的搜索技术来确定应在每个时间步应用于每个平台的推力，并且计划范围可以是一步（贪婪）或两步。后评估Cramer-Rao下界（PCRLB）是用于评估每个潜在传感器操纵（推力）的性能度量，它可提供最佳（最低均方误差）跟踪性能。我们利用一种新颖的方法来近似PCRLB用于机动目标跟踪（“最适合的高斯”（BFG）方法：Hernandez等，2005）。封闭形式的表达式给出了每个采样时间的BFG近似值。因此，可以以非常低的计算开销来近似PCRLB。因此，即使在时间紧迫的机载领域，也可以实施规划工具，以实时进行决策。 MASTER的功能使人们可以在一系列传感器目标场景中访问计划工具的性能，从而可以确定传感器的最低要求，以满足任务要求。

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