Novel Target Tracking Methods for Combining Passive and Active Sensors

列奥纳多制造有源传感器和无源传感器，用于例如空中平台（例如F16和无人机）。有源传感器，如雷达，可以提供准确的范围信息，但发射辐射这样做，降低了感知的程度可以是隐蔽的。相反，被动传感器，如相机，不以相同的方式发射辐射，但不容易配置为提供范围信息。这个学生奖学金涉及融合来自这些传感器的信息，以最大限度地提高整体系统性能和传感的隐蔽性。传统上，来自这些传感器的信息在高度抽象的层次上进行组合。然而，随着通信带宽和处理能力的不断提高，可以考虑在较低的抽象级别上融合数据。这应该允许一种认知方法来融合信息，使得传感器的操作能够实时地进行调整，以显式地最大化性能和隐蔽性：例如，雷达最初可能用于推断物体的范围，但随后可能会使用相机来保持对物体的跟踪。这样的认知能力，预计将提高目标跟踪的保真度和准确性，特别是。高性能的目标跟踪算法，如粒子滤波器，是基于顺序贝叶斯推理。这使得算法能够充分利用任何可用的信息-例如位置和速度的测量，以及颜色等其他属性。这样建立的算法至少在理论上可以迎合例如具有不同更新速率的不同个体传感器、导致测量无序到达的通信延迟、传感器未对准、系统偏差以及使用属性来区分多个对象轨迹。以及解决这些挑战，新的扩展，这样的算法将需要开发，以支持认知能力的发展。因此，该奖学金可能会借鉴最近的发展，如顺序蒙特卡罗（SMC）采样器;近似通常用于贝叶斯网络的上下文中，如结构化平均场，置信传播和菊池approximations.The博士旨在开发这些方法，并在多个被动，主动传感器和/或多功能传感器的输入组合的情况下演示性能。我们的目标是提高目标跟踪的准确性和鲁棒性，通过使用先进的方法来处理数据，但也通过调整各个传感器的操作，使它们协同工作，以提供认知能力。

Leonardo manufacture both active sensors and passive sensors for use with, for example, air platforms (e.g. F16s and drones). Active sensors, such as radars, can provide accurate range information but emit radiation to do so, reducing the extent to which sensing can be covert. Conversely, passive sensors, such as cameras, do not emit radiation in the same way, but are not easily configured to provide range information. This studentship relates to fusing the information from such sensors to maximise both the overall systems performance and the covert nature of the sensing.Traditionally, the information from such sensors is combined at a high level of abstraction. However, the increasing availability of communications bandwidth and processing power make it possible to consider fusing the data at a lower level of abstraction. This should permit a cognitive approach to the fusion of information such that the sensors operation are adapted in real-time to explicitly maximise performance and covertness: for example, a radar might be used initially to infer an objects range but a camera might subsequently be used to maintain a track on the object. Such a cognitive capability is anticipated to improve the fidelity and accuracy of target tracking, in particular.High performance target tracking algorithms, such as particle filters, are based on sequential Bayesian inference. This enables the algorithms to make best use of whatever information is available - such as measurements of position and speed, but also additional attributes such as colour. Such established algorithms can, at least in theory, cater with, for example, different individual sensors having different update rates, communication delays causing measurements to arrive out of order, sensor misalignments, systematic biases and using attributes to distinguish multiple objects tracks. As well as addressing these challenges, novel extensions to such algorithms will need to be developed to support the development of a cognitive capability. The studentship is therefore likely to draw on recent developments such as Sequential Monte Carlo (SMC) samplers; approximations commonly used in the context of Bayesian Networks, such as Structured Mean Field, Belief Propagation and Kikuchi approximations.The PhD aims to develop these methods and to demonstrate performance in the context of a combination of inputs from multiple passive, active sensors and/or multi-function sensors. The objective is to enhance the accuracy and robustness of target tracking by both using advanced methods for processing the data but also by adapting the operation of the individual sensors such that they work synergistically to provide a cognitive capability.