Vehicle tracking using a network of small acoustic arrays

Major advances in base technologies of computer processors and low cost communications have paved the way for a resurgence of interest in unattended ground sensors. Networks of sensors offer the potential of low cost persistent surveillance capability in any area that the sensor network can be placed. Key to this is the choice of sensor on each node. If the system is to be randomly deployed then nonline of sight sensor become a necessity. Acoustic sensors potentially offer the greatest level of capability and will be considered here. As a passive sensor, only time of arrival or bearing information can be obtained from an acoustic array, thus the tracking of targets must be done in this domain. This work explores the critical step between array processing and implementation of the tracking algorithm. Specifically, unlike previous implementations of such a system, the bearings from each frequency interval of interest are not averaged but are used as data points within a Kalman filter. Thus data is not averaged and then filtered but all data is put into the tracking filter.