A sensor reduction technique using Bellman optimal estimates of target agent dynamics

Reducing the number of sensors in a sensor network is of great interest for a variety of surveillance and target tracking scenarios. The time and resources needed to process the data from additional sensors can delay reaction time to immediate threats and consume extra financial resources. There are many methods to reduce the number of sensors by considering hardware capabilities alone. However, by incorporating an estimate of environment and agent dynamics, sensor reduction for a given scenario may be achieved using Bellman optimality principles. We propose a method that determines the capture regions where sensors can be eliminated. A capture region is defined as a section of the surveillance field, where using a causal relationship to the other sensors, an event may be determined using fast marching semi-Lagrangian (FMSL) solution techniques. This method is applied to a crowded hallway scenario with two possible exits, one primary, and one alternate. It is desired to determine if a target deviates from the crowd and moves toward the alternate exit. A proximity sensor grid is placed above the crowd to record the number of people that pass through the hallway. Our result shows that the Bellman optimal approximation of the capture set for the alternate exit identifies the region of the surveillance field where sensors are needed, allowing the others to be removed.