Target location estimation in sensor networks using range information
Antonio Artés-Rodrı́guez, Marcelino Lázaro, Lang Tong · 2005
We consider the problem of target location estimation in the context of large scale, dense sensor networks. We model the probability of detection in each sensor, p/sub d/ as a function of the distance between the sensor and the target. Based on a binary (detection vs. no detection) information from each sensor and the model of p/sub d/, we propose two different fusion rules for estimating the target location: a maximum likelihood estimate and an empirical risk minimization method. Moreover, we also consider the case where only sensors with a positive detection transmit their reading. This can be helpful to economize the power of sensor units. By employing Gaussian like p/sub d/ models, we develop versions of both methods based on simple initialization procedures and a gradient search. We compare and discuss both algorithms in terms of complexity and accuracy.