Optimal design of sensor networks for vehicle detection, classification, and monitoring

The tracking and identification of vehicles for the purpose of surveillance is a widespread application. Observations from a network of sensors can be used to make decisions regarding the identity of vehicles, as well as their trajectories. Generally, the information provided by a sensor network is limited, so vehicles may be misclassified, go undetected, and/or their trajectories may not be determined uniquely. Often, assumptions are made regarding, for example, traffic composition and possible vehicle trajectories. Because the performance of a sensor network can be sensitive to these assumptions, the conclusions made by the network about the identity and trajectory of vehicles can be highly inaccurate. In this paper, these assumptions are treated as possible models of reality that are subsequently evaluated in a decision framework. Mathematical models for vehicle movement and sensor behavior are developed. Candidate designs for the sensor network are considered, where each design is defined by the number, location, and range of the sensors. Methods from decision theory are used to determine the optimal design for the sensor network.