Polarization-cum-energy metric for footstep detection using vector-sensor

We address the problem of human footstep detection using data recorded by a single tri-axial geophone. It is observed that footstep signature recorded using a vector-sensor is characterized by signal polarization, which, when exploited effectively, has the capability to identify footsteps at increasing source-sensor distances compared to existing techniques. We quantify the effect of signal polarization by fitting a great-arc using spherical linear interpolation (SLERP) to the data vectors after normalization. Furthermore, the signal polarization metric, which provides extended detection range, is combined with signal energy to form a robust polarization-cum-energy metric for efficient detection. Experimental results are presented to substantiate the performance of this technique.