Resource management for wireless networks of bearings-only sensors

Resource management;Kalman filters;Wireless networks

Le, Qiang ; Electrical and Computer Engineering

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Resource management;    Kalman filters;    Wireless networks;   

Others  :  https://smartech.gatech.edu/bitstream/1853/10548/1/le_qiang_200605_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The thesis focuses on resource management or sensor allocation when we use bearings-only measurements to track targets in an unattendedground sensor (UGS) network. Intelligent resource management is necessary because each UGS sensor node has limited power and it isdesirable that estimation performance not degrade very much when only a few nodes are active to maximize the effective tracking lifetime. For scheduling to prolong the tracking lifetime, a new energy-based (EB) metric is proposed to model the number of snapshots remaining for a hypothesized node set, i.e., the remaining battery energy divided by the energy to sense and share information amongst the node set. Unlike other methods that use the totalenergy consumed for the given snapshot as the energy-based metric, the new EB metric can achieve load balancing of the nodes without resorting to computationally demanding non-myopic optimization. The metrics to choose nodes at a given snapshot could be geometry-based (GB) to minimize the estimation error, EB, or multiobjective. In determining the active set, each node only knows the existence of itself, theactive set of nodes from the previous snapshot and the node's neighbors, i.e., the set of nodes within a distance of r_nei. When measuring the tracking lifetime of the system, we propose anadaptive transmission range control, known as the knowledge pool (KP) where the transmission range is determined by the knowledge of the network and the currently remaining battery level. The KP saves more energy usage than another adaptive transmission range controlbounded with the GB metric when the global location information is available. We also provide practical search algorithms to optimize aconstraint metric (multiobjective function) using one metric as the optimization metric under the constraint of the other. We alsodemonstrate the resource management schemes for multitarget tracking with the field data.