Noise Mitigation for Multiple Target Tracking in Acoustic Wireless Sensor Networks

The wireless acoustic sensors are not only subject to Doppler effect when being used to tracking high speed ground vehicles but also are prone to be disturbed by environmental noises generated locally by, for example, small animal passing or foliage blowing. These noises cannot be expressed using common analytic forms such as white or Gaussian noises as they impact portions of the sensor field sporadically. These noises interfere target sound detection and so disrupt multiple target tracking by the sensors, especially when those sensors have no capability to discern the sound sources and to communicate between sensor nodes due to their limited power. In this work, we propose an environmental noise model that is based on the noise data gathered from the fields and develop a heuristic noise mitigation algorithm for tracking multiple targets by the acoustic sensors with the limited capability. After filtering the noises with the noise mitigation algorithm, we track the multiple targets by using the rule based multiple target tracking algorithm developed in our previous study. Our results show that the tracker with the noise mitigation algorithm tracks multiple targets reliably with low computational complexity in the noisy acoustic wireless sensor network environments as long as the target sensing ranges are larger than the sensor separation.