基于自适应深度射线树的WLAN室内目标入侵检测算法

WLAN indoor target intrusion detection algorithm based on adaptive-depth ray tree

由于无线局域网(wireless local area network, WLAN)的广泛部署和智能终端对WLAN协议的普遍支持,本文提出一种基于自适应深度射线树的WLAN室内目标入侵检测算法,其利用现有WLAN基础设施即可实现未携带任何信号收发设备的室内目标的入侵检测.为此,首先建立基于自适应深度射线树的准三维射线追踪模型,对室内静默和入侵状态下的接收信号强度(received signalstrength, RSS)传播特性进行建模;其次,联合RSS均值、方差、最大值、最小值、极差值和中位值6种信号特征构建概率神经网络(probabilistic neural network, PNN)的训练数据库;最后,利用训练得到的PNN对新采集RSS数据进行多分类判决,进而实现对室内目标的入侵检测与区域定位.实验结果表明,本文所提算法具有较高的入侵检测概率和较低的数据库构建开销.

With the wide deployment of wireless local area network(WLAN) and general support of WLAN protocol by various intelligent terminals, intrusion detection with respect to an indoor target can be realized using the existing WLAN infrastructure. To this end, the adaptive-depth ray tree-based quasi 3 D ray-tracing model is constructed to model the received signal strength(RSS) propagation property under indoor silence and intrusion scenarios. Then, the RSS mean, variance, maximum, minimum, range, and median are allied to construct the training database for a probabilistic neural network(PNN). Finally, after the training, this PNN is utilized to perform the multi-classification decision with respect to the newly-collected RSS data and to consequently achieve indoor target intrusion detection and area localization. The experimental results indicate that the proposed algorithm has a high intrusion detection rate and low database construction cost.