利用神经网络进行扩展检测

L. Cano
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引用次数: 0

摘要

特别是在保护高价值资产时,需要扩展检测(ED)。物理防护系统(PPS)通常将检测、延迟和响应(DDR)元素集成在一起,以在明确定义的周界内评估威胁。PPS外围的态势感知(SA)需要使用更远距离的传感器系统,如雷达或无人值守的地面传感器,覆盖相对较大的区域。收集这些传感器数据,特别是在高噪声环境中,对构建可靠的ED系统提出了严峻的挑战。使用神经网络合并传感器数据并识别潜在威胁可以使SA系统得到更广泛的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Neural Networks for Extended Detection
Extended Detection (ED) has become required especially when protecting high valued assets. Physical Protection Systems (PPS) usually integrate Detection, Delay, and Response (DDR) elements in a manner to assess threats at well defined perimeters. Situational Awareness (SA) beyond PPS perimeters requires the use of longer range sensors systems such as Radars or Unattended Ground Sensors which cover relatively large areas. Gathering such sensor data, especially in high noise environments poses a serious challenge to building reliable ED systems. The use of Neural Networks to merge sensor data and identify potential threats can make SA systems available for broader use.
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