A Sub-Optimal Hierarchical Maximum Likelihood Algorithm for Collaborative Localization in Ad-Hoc Networks

This paper addresses indoor localization techniques through ad-hoc wireless networks, where anchors and unknown nodes are randomly positioned in a squared area. The position of unknown nodes is estimated starting from received signal strength (RSSI) measurements, since nodes are assumed to be not equipped with specialized localization hardware. The possibility to perform power measurements among any couple of nodes (either anchors or unknown nodes) enables the exploitation of collaborative localization techniques. In this case, the complexity of the optimum maximum likelihood (ML) technique becomes too huge to have any practical interest. Thus, we propose a sub-optimal but computational efficient, hierarchical algorithm to solve the ML problem. The performance in terms of mean localization error is evaluated by simulations for different scenarios and taking a realistic channel model into account. The sensitivity of the estimated position to the uncertainty on channel parameters is also evaluated.