Target location estimation in sensor networks using range information

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_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_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_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.