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