This paper describes the development of a tool that predicts the coverage and performance of sensor networks. Specifically it examines weapon locating radars and acoustic sensors in different terrain and weather conditions. The computer environment and multiple sensor models are presented. Fusion of sensors takes multiple predicted accuracy metrics from the single sensor performance models and combines them to show networked performance. Calculations include Cramer-Rao lower bound computation of the sensors and the fused sensors source location error. Results are presented showing the outputs of the models in the form of sensor accuracy maps superimposed onto terrain maps.
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 non line of sight sensor become a necessity. Acoustic sensors potentially offer the greatest level of capability and will be considered here. In addition, there is a trade off between sensor density and tracking technique that will impact on cost. 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 paper 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.