Tracking targets in quantized areas with wireless sensor networks

Target tracking is an important application of sensor networks, particularly interesting in applications for Ecology for monitoring and tracking animals. In this context, understanding the movement pattern and the territorial occupation of animals are fundamental for understanding their habits. In practice, target tracking often operates on quantized areas (divided into cells). In this work, we propose and evaluate a quantized target tracking approach in such a way that the network is organized in a grid, where each cell is a region occupied by the target (animal). The cell size is determined according to the desired granularity. The computation of the target´s position obeys a voting scheme, so the technique is simple and low cost. To estimate the target´s position, we use the Kalman or Particle filters. Results show that position computation errors are close to two cells, depending on the scenario.