Dynamic Multidimensional Scaling for Low-Complexity Mobile Network Tracking

Abstract

Cooperative localization of mobile sensor networks is a fundamental problem which becomes challenging for anchorless networks where there is no pre-existing infrastructure to rely on. Two cooperative mobile network tracking algorithms based on novel dynamic multidimensional scaling (MDS) ideas are proposed. The algorithms are also extended to operate in partially connected networks. Compared with recently proposed algorithms based on the extended and unscented Kalman filter (EKF and UKF), the proposed algorithms have a considerably lower computational complexity. Furthermore, model-independence, scalability, as well as an acceptable accuracy make our proposed algorithms a good choice for practical mobile network tracking.