Co-operative sensor localization using maximum likelihood estimation algorithm

Abstract

In wireless sensor networks, self-localizing sensors are required in a wide variety of applications, from environmental monitoring and manufacturing logistics to geographic routing. In sensor networks which measure high-dimensional data, data localization is also a means to visualize the relationships between sensors’ high dimensional data in a low-dimensional display.This thesis considers both to be part of the general problem of estimating the coordinates of networked sensors. Sensor network localization is ‘cooperative’ in the sense that sensors work locally, with neighboring sensors in the network, to measure relative location, and then estimate a global map of the network.The choice of sensor measurement technology plays a major role in the network’s localization accuracy, energy and bandwidth efficiency, and device cost. This thesis considers measurements of time-of-arrival(TOA), received signal strength (RSS), quantized received signal strength (QRSS), and connectivity. I have taken the simulated data taking varity position of the sensor. From these different position the Cram´er-Rao lower bounds on the variance possible from unbiased location estimators are derived and studied. In this CRB calculation I have taken the RSS case only. Maximum Likelihood estimation algorithm is studied and applied for a particular node position.