Fractal Dimension Based on Morphological Covering for Ground Target Classification

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.

[1]  Moncef Gabbouj,et al.  Multi-Scale Edge Detection and Object Extraction for Image Retrieval , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[2]  Nacim Betrouni,et al.  Fractal and multifractal analysis: A review , 2009, Medical Image Anal..

[3]  Takayuki Hirata,et al.  A correlation between the b value and the fractal dimension of earthquakes , 1989 .

[4]  Joseph Mathew,et al.  USING THE CORRELATION DIMENSION FOR VIBRATION FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS—I. BASIC CONCEPTS , 1996 .

[5]  H. Schellnhuber,et al.  Efficient box-counting determination of generalized fractal dimensions. , 1990, Physical review. A, Atomic, molecular, and optical physics.

[6]  Petros Maragos,et al.  Measuring the Fractal Dimension of Signals: Morphological Covers and Iterative Optimization , 1993, IEEE Trans. Signal Process..

[7]  S. Nahavandi,et al.  A novel application of a microaccelerometer for target classification , 2004, IEEE Sensors Journal.

[8]  A. Tarquis,et al.  Comparison of gliding box and box-counting methods in soil image analysis , 2006 .

[9]  P. Agostino Accardo,et al.  Use of the fractal dimension for the analysis of electroencephalographic time series , 1997, Biological Cybernetics.

[10]  P. Maragos,et al.  Fractal dimensions of speech sounds: computation and application to automatic speech recognition. , 1999, The Journal of the Acoustical Society of America.

[11]  Cote,et al.  Box-counting multifractal analysis. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[12]  P. Grassberger.  Generalized dimensions of strange attractors , 1983 .

[13]  gulsev uyar aldas,et al.  Non-Linear Behavior of Blasting Noticed on Seismic Signals , 2010 .

[14]  Joseph Mathew,et al.  USING THE CORRELATION DIMENSION FOR VIBRATION FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS—II. SELECTION OF EXPERIMENTAL PARAMETERS , 1996 .

[15]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[16]  Yixin Yin,et al.  Recognition of moving ground targets by measuring and processing seismic signal , 2005 .

[17]  James H. Brown,et al.  The fourth dimension of life: fractal geometry and allometric scaling of organisms. , 1999, Science.

[18]  Bing Li,et al.  Morphological covering based generalized dimension for gear fault diagnosis , 2012 .

[19]  D-S Liu,et al.  Multi-scale fractal dimension based on morphological covering for gear fault diagnosis , 2011 .

[20]  Rongchun Zhao,et al.  Morphology-based multifractal estimation for texture segmentation , 2006, IEEE Transactions on Image Processing.

[21]  N. N. Oiwa,et al.  A moving-box algorithm to estimate generalized dimensions and the f (a) spectrum , 1998 .

[22]  Sim Heng Ong,et al.  A practical method for estimating fractal dimension , 1995, Pattern Recognit. Lett..