TY - GEN
T1 - Applying matching pursuit decomposition time-frequency processing to UGS footstep classification
AU - Larsen, Brett W.
AU - Chung, Hugh
AU - Dominguez, Alfonso
AU - Sciacca, Jacob
AU - Kovvali, Narayan
AU - Papandreou-Suppappola, Antonia
AU - Allee, David
PY - 2013/8/9
Y1 - 2013/8/9
N2 - The challenge of rapid footstep detection and classification in remote locations has long been an important area of study for defense technology and national security. Also, as the military seeks to create effective and disposable unattended ground sensors (UGS), computational complexity and power consumption have become essential considerations in the development of classification techniques. In response to these issues, a research project at the Flexible Display Center at Arizona State University (ASU) has experimented with footstep classification using the matching pursuit decomposition (MPD) time-frequency analysis method. The MPD provides a parsimonious signal representation by iteratively selecting matched signal components from a pre-determined dictionary. The resulting time-frequency representation of the decomposed signal provides distinctive features for different types of footsteps, including footsteps during walking or running activities. The MPD features were used in a Bayesian classification method to successfully distinguish between the different activities. The computational cost of the iterative MPD algorithm was reduced, without significant loss in performance, using a modified MPD with a dictionary consisting of signals matched to cadence temporal gait patterns obtained from real seismic measurements. The classification results were demonstrated with real data from footsteps under various conditions recorded using a low-cost seismic sensor.
AB - The challenge of rapid footstep detection and classification in remote locations has long been an important area of study for defense technology and national security. Also, as the military seeks to create effective and disposable unattended ground sensors (UGS), computational complexity and power consumption have become essential considerations in the development of classification techniques. In response to these issues, a research project at the Flexible Display Center at Arizona State University (ASU) has experimented with footstep classification using the matching pursuit decomposition (MPD) time-frequency analysis method. The MPD provides a parsimonious signal representation by iteratively selecting matched signal components from a pre-determined dictionary. The resulting time-frequency representation of the decomposed signal provides distinctive features for different types of footsteps, including footsteps during walking or running activities. The MPD features were used in a Bayesian classification method to successfully distinguish between the different activities. The computational cost of the iterative MPD algorithm was reduced, without significant loss in performance, using a modified MPD with a dictionary consisting of signals matched to cadence temporal gait patterns obtained from real seismic measurements. The classification results were demonstrated with real data from footsteps under various conditions recorded using a low-cost seismic sensor.
KW - Bayesian classification
KW - Feature extraction
KW - Footstep classification
KW - Matching pursuit decomposition
KW - Seismic sensing
KW - Time-frequency analysis
KW - Unattended ground sensor
UR - http://www.scopus.com/inward/record.url?scp=84881102146&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881102146&partnerID=8YFLogxK
U2 - 10.1117/12.2015498
DO - 10.1117/12.2015498
M3 - Conference contribution
AN - SCOPUS:84881102146
SN - 9780819495020
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XII
T2 - Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XII
Y2 - 29 April 2013 through 1 May 2013
ER -