The application of dynamic synapse neural networks on footstep and vehicle recognition

In this paper we report application of biologically based dynamic synapse neural network (DSNN) on perimeter protection. More specifically, the purpose is to protect a fence line from approaching human being and vehicles. We have used geophones to detect seismic signals generated by footsteps and vehicles. While acoustic sensors can be fooled by artificial sounds, fooling geophones by artificial seismic waves is a complicated task. Moreover detecting human footsteps - weak signal to noise ratio - by acoustic waveform is subject to the distance between the sensor and human. Therefore detecting a human's footsteps by employing acoustic information will not be possible unless he/she walks close to the acoustic sensors. Geophones are resonant devices; therefore any vibration in the substrate can generate seismic waveforms which could be very similar to the signature generated by footstep or vehicle. In addition, geophone response is completely substrate dependent, rendering recognition of footsteps or vehicle vs. other vibrations to be a very difficult task. Therefore, in order to have robust and high-confidence classification/detection of a human/vehicle threats, we have employed the DSNN. The network is trained to extract intrinsic characteristics of the waveform, frame by frame. Then parameters of the network are analyzed by Gaussian mixture models. The results of our study show 88.8% and 86% correct classification rate for the detection of human footsteps and vehicle respectively.