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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 t h e target. Based on a binary (dctection 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. T h i s can be helpful to economize the power of sensor units. By employing gaussian like pd models, we develop versions of both methods based on simple initialization procedures and ; I gradient search. We compare and discuss both algorithms in terms of complexity and accuracy.