School of
Information Technology and Electrical Engineering

Speaker: Mr Vikram Kumar
Seminar Date: Fri, 08/12/2017 - 09:00
Venue: 78-344
Host: Prof Neil Bergmann

Seminar Type:  PhD Thesis Review

Abstract: 

Tracking mobile agents has received significant scientific and commercial interest. However, due to issues such as the high energy consumption of receivers of global navigation satellite systems, long-term tracking of resource-constrained mobile agents remains challenging. Cooperative position tracking has been proposed to improve energy efficiency, albeit most existing schemes perform opportunistic cooperation and optimize for either energy or accuracy. In this thesis, assuming the existence of a reasonably stable group of mobile nodes such as people, animals, or portable assets, a cluster-based cooperative tracking algorithm is proposed for planned cooperation where cluster head centrally coordinates resource usage among cluster members. Several variants of the proposed cluster-based algorithm, differentiated based on how a node estimate its position after receiving a position update from the cluster head, are investigated. Examples include assuming a node's position to be the same as the position update received from the cluster head or using a Kalman filter to incorporate the inertial measurement sensor data as well as the received position updates. To further improve the localization accuracy at an instance of tracking, the widely-available received signal strength indicator (RSSI) measurements are utilized through multilateration. The theoretical bounds for localization performance are derived in a practical scenario where both neighbouring node positions and RSSI measurements are perturbed according to widely-accepted perturbation models. Targeting the resource-constrained nodes, two different self-localization algorithms are proposed, which vary in computational complexity and localization performance. Finally, a multi-mode tracking algorithm is proposed building on the proposed cluster-based tracking and self-localization algorithms. The proposed algorithms are evaluated on position tracks of nodes generated using Reynolds' flocking model. Overall, the proposed algorithms show significant advantages in localization and tracking of mobile agents.

Biography: 

Vikram Kumar received his B.Tech. (Information Technology) in 2006 and M. Tech. (Computer Science Engineering) in 2008 from Himachal Pradesh University, India. Currently, he is doing Ph.D. at the School of Information Technology and Electrical Engineering, the University of Queensland in collaboration with the Distributed Sensor Systems group at CSIRO/Data61. His research interests include energy-efficiency localization and tracking.