Target location estimation in sensor networks using range information

• Published in: 2004 IEEE Sensor Array and Multichannel Signal Processing Workshop pp. 608 - 612
• Main Authors: Artes-Rodriguez, A., Lazaro, M., Tong, L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2004
• Subjects: Computer networks; Intelligent networks; Large scale integration; Large-scale systems; Maximum likelihood detection; Risk management; Sensor fusion; Target tracking; Wireless communication; Wireless sensor networks
• ISBN: 9780780385450; 0780385454
• DOI: 10.1109/SAM.2004.1503021

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.