A Novel Dynamic Filter Switching Algorithm to Track People Using Acoustic Sensors

We present a new dynamic filter switching algorithm to track people that randomly enter, exit, move and stop in a region of interest using a network of uniformly spaced, stationary acoustic sensors. The existence of a new target is determined by jointly weighting the particles of a track-before-detect particle filter and an interacting multiple model particle filter which is used to track confirmed targets. The algorithm detects new targets as well as tracks targets with intermittent motion, as is shown by Monte Carlo simulations.