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Real-time multi-sensor data fusion for target detection, classification, tracking, counting, and range estimates

机译:实时多传感器数据融合,用于目标检测,分类,跟踪,计数和范围估计

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摘要

As part of the Commanding General of Army Material Command's Research, Development & Engineering Command (RDECOM), the U.S. Army Research Development and Engineering Center (ARDEC), Picatinny funded a joint development effort with McQ Associates, Inc. to develop an Advanced Minefield Sensor (AMS) as a technology evaluation prototype for the Anti-Personnel Landmine Alternatives (APLA) Track III program. This effort laid the fundamental groundwork of smart sensors for detection and classification of targets, identification of combatant or non-combatant, target location and tracking at and between sensors, fusion of information across targets and sensors, and automatic situation awareness to the 1st responder. The efforts have culminated in developing a performance oriented architecture meeting the requirements of size, weight, and power (SWAP). The integrated digital signal processor (DSP) paradigm is capable of computing signals from sensor modalities to extract needed information within either a 360° or fixed field of view with acceptable false alarm rate. This paper discusses the challenges in the developments of such a sensor, focusing on achieving reasonable operating ranges, achieving low power, small size and low cost, and applications for extensions of this technology.
机译:作为陆军物质指挥的研究,发展和工程指挥(RDecom),美国陆军研究开发和工程中心(ARDEC)的一部分,PICATINNY与MCQ Associates,Inc。开发一个先进的雷区传感器(AMS)作为杀伤人员地雷替代品(APLA)跟踪III计划的技术评估原型。这项努力为智能传感器奠定了基本的基础,用于检测和分类目标,识别战斗机或非战斗人员,目标位置以及在传感器之间以及在传感器之间进行跟踪,跨越目标和传感器的信息融合,以及对第一个响应者的自动情况意识。努力在开发满足规模,重量和功率(交换)要求的表现面向架构方面的努力。集成数字信号处理器(DSP)范例能够计算来自传感器模式的信号,以提取360°或以可接受的误报率的固定视野中所需的信息。本文讨论了这种传感器发展的挑战,重点是实现合理的操作范围,实现低功耗,小尺寸和低成本,以及该技术的扩展应用。

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