Sensor network based localization and target tracking through hybridization in the operational domains of beamforming and dynamic space-time clustering

The severe power, time and processing constraints on ad hoc wireless sensor networks for area surveillance require in-situ adaptations to conserve resources and optimize performance. In particular, it may be necessary to make dynamic tradeoffs between centralized processing algorithms, like beamforming, and knowledge based distributed processing algorithms like dynamic space-time clustering (DSTC) that rely on local processing of raw sensor data. Beamforming methods can achieve high levels of accuracy in estimating direction of arrival with a sound wave even when the source is in the far field. Hence accurate localization can be achieved with a relatively sparse sensor network. However, beamforming has severe limitations when the number of nodes increases. It requires orders of magnitude higher energy for transporting the entire time series over the network. DSTC methods, on the other hand, work well when the number of nodes is large because clusters can be formed within a smaller space-time window. This work examines the operational domains of the two centralized and distributed algorithms by analyzing sources of error, dependence on sensor density, sensor geometries, energy usage, control logic for data processing and the effects of network topology on the two algorithms. Based on this analysis, we develop hybrid algorithms that take advantage of the operational characteristics of each one in designing a high performance sensor network.