Information-Driven Blind Doppler Shift Estimation and Compensation Methods for Underwater Wireless Sensor Networks.

摘要

We investigated different methods for blind Doppler shift estimation and compensation in underwater acoustic wireless sensor networks. Our study is based on the data collected from our underwater experiments. We analyzed the data collected by using non-data aided techniques such as Power Spectrum analysis, Autocorrelation, and Squaring Time Phase Recovery (Oerder & Meyr) to estimate Doppler shift in collaborative distributed underwater sensor networks. We also extensively investigated optimal sensor placement in a tree structured multi-hop hierarchical network. We focused on a symmetric tree like multi-hop hierarchical routing topology, which can potentially cover larger areas as we go deeper in the water than flat placement of underwater sensor networks. We used information theoretic tools such as entropy, conditional entropy, probability mass function etc. of input and output data signal to analyze the mutual information, information loss, bit error rate, and channel capacity. We focused on the optimizing mutual information between transmitted data and received data for a specific sensor network in underwater channel. Specifically, we used multiple sensor nodes in a relay network to transfer the data into long distance looking for maximum mutual information. In addition, we studied the relationship of channel capacity to transmitted power, bandwidth and carrier frequency.