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Sensor Data Association for the Seawolf Mid-Life Update (SWMLU) Programme

机译:海狼中年更新(SWMLU)计划的传感器数据协会

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The Seawolf Mid-Life Update (SWMLU) programme is a major upgrade of the UK Royal Navy's principal point defence weapon system. The addition of an Electro-Optic sensor to the pre-existing 'I' and 'K' band radars presents a significant engineering challenge. The processing of the data from the 3 sensors into a fused picture such that a coherent view of which objects represent targets and own missiles is a key element of the overall system design and is critical to achieving the required system performance. Without this coherent view of the detected objects incorrect guidance commands will be issued to the Seawolf missiles resulting in a failure to successfully intercept the target. This paper reviews the sensor data association problem as it relates to the SWMLU system and outlines identified solution strategies. The SWMLU sensors provide complementary data that can be exploited through data association to maximise tracking accuracy as well as maintaining performance under sensor lost-lock conditions. The sensor data association approach utilises radar and EO properties from spatial and temporal domains. These characteristics are discussed in terms of their contribution to the SWMLU data association problem where it will be shown that the use of object attributes from the EO sensor and their behaviour over time is a critical performance factor.
机译:Seawolf Mid-Life Update(SWMLU)计划是英国皇家海军主要点防御武器系统的重大升级。在现有的“ I”和“ K”波段雷达中增加光电传感器带来了巨大的工程挑战。将来自3个传感器的数据处理成融合图像,从而使哪些物体代表目标和自己的导弹的连贯视图是整个系统设计的关键要素,对于实现所需的系统性能至关重要。没有检测到的物体的这种连贯的视图,将向海狼导弹发出错误的制导命令,从而导致无法成功拦截目标。本文回顾了与SWMLU系统相关的传感器数据关联问题,并概述了已确定的解决方案策略。 SWMLU传感器提供补充数据,可通过数据关联来利用这些数据,以最大化跟踪精度并在传感器失锁条件下保持性能。传感器数据关联方法利用了来自时空域的雷达和EO属性。根据这些特征对SWMLU数据关联问题的贡献来讨论这些特征,其中将显示,使用EO传感器的对象属性及其随时间的行为是关键的性能因素。

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