Finally, conclusions and future work are discussed in Section 6.2.?Theoretical BackgroundEarly publications regarding automated acoustic vehicle recognition algorithms were focused mainly on military vehicle signals, in order to develop a system that improves surveillance for security [4�C6]. Compared to acoustic signals, using seismic signals for detection allows the performance to be independent of wind conditions, which can often cause difficulties in acoustic detection. Moreover, seismic waves are less sensitive to factors such as acoustic noise, Doppler effects. Seismic sensors offer many benefits over acoustic and magnetic sensors, in that the propagation through the earth is less sensitive to atmospheric conditions, such as wind, moisture, and temperature [7,8].

Seismic sensors also provide non-line-of-sight detection capabilities for vehicles at significant ranges [9�C11]. Seismic sensors provide good detection range, increased detection capabilities and have been extensively used in many applications [12�C14]. Xin Jin et al. [15] presents a symbolic feature extraction method for target detection and classification, where the features are extracted as statistical patterns by symbolic dynamic modeling of the wavelet coefficients generated from time series of seismic and PIR sensors. The potential of exploiting seismic surface waves, in particular Rayleigh waves, for military vehicle and personnel tracking was investigated [16,17]. J. Huang et al. [18] proposed wavelet packet manifold (WPM) which provides a more robust representation for seismic target classification in Unattended ground sensor (UGS) systems.

Dan Li et al. [19] have provided some promising preliminary results on classifying between wheeled and tracked vehicles. Outcome was positive regarding military vehicles. However, it was found their method was not suitable for non-military passenger vehicles as signals generated by military are louder and more distinguishable [4,9,20,21].2.1. Vehicle Induced Seismic WavesIn the field of pavement dynamics, a vehicle can be regarded as a set of moving loads acting on the pavement, with the pavement modeled as a beam, plate or a multi-layered system on a viscoelastic foundation [22]. Within this framework, a source-path-receiver scenario can be used to characterize vehicles in terms of induced seismic signals.

Vehicles’ contact with irregularities on the road surface induce dynamic loads on the pavement [23]. When Dacomitinib a vehicle, such as a car or a truck, strikes an irregularity on the road surface, it generates an impact load and an oscillating load due to the subsequent ��axle hop�� of the vehicle. The impact load generates seismic waves that are predominant at the natural seismic frequencies of the soil whereas the axle hop generates seismic excitation at the hop frequency.