Abstract
Seismic waves are widely used in ground target classification due to its inherent characteristics. However, they are often affected by extraneous factors and have been found to demonstrate a complicated nonlinear characteristic. The traditional signal analysis methods cannot effectively extract the nonlinear features. Motivated by this fact, this paper applies the fractal dimension (FD) based on morphological covering (MC) method to extract features of the seismic signals for ground targets classification. With the data measured from test field, three different schemes based on MC method are employed to classify tracked vehicle and wheeled vehicle in different operation conditions. Experiment results demonstrate that the three proposed methods achieve more than 90% accuracy for vehicle classification.
Highlights
Detecting the moving ground targets has two ways
This paper has applied fractal dimension (FD) based on morphological covering (MC) method to extract features of seismic waves for ground targets
The main difference between the MC method and the BC method is the utilization of the morphological cover to replace the regular box cover
Summary
Detecting the moving ground targets has two ways. One way is active detection technology, including sonar and radar. Due to large volume and wave launch, this kind of detection equipment is not fit to be used in battlefield surveillance Another way called passive detection technology, which applies sensors to record signals produced by targets, is cheaper and safer than the former one. Shock and Vibration dimension based on MC method can effectively extract the features of mechanical fault through the analysis of the vibration signals [17, 18]. We have first investigated the application of FD based on MC method for characterizing and classifying the seismic waves generated by tracked vehicle and wheeled vehicle.
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