Networked Information Gathering in Stochastic Sensor Networks: Compressive Sensing, Adaptive Network Coding and Robustness
Abstract
This project has studied networked information gathering, spanning from sensing to processing to communications. The first main thrust was devoted to building a deep understanding of efficient multicast compressively sampled signals from a source to many receivers, over lossy wireless channels. Based on extreme value theory, the network outage is characterized in terms of key system parameters, including the erasure probability, the number of receivers and the sparse structure of the signal, for both cases where the transmitter may or may not be capable of reconstructing the compressively sampled signals. The second thrust focused on the fundamental Doppler sensing capability in a networked radar system. This work has taken some initial steps to develop a novel model in which each radar employs Doppler processing to eliminate clutters from its received signal and decision fusion is carried out across multiple radars for detection. With this model, the optimal detection decision rule is derived for maximizing the detection probability subject to a certain false alarm probability.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 11, 2013
- Accession Number
- ADA590144
Entities
People
- Junshan Zhang
Organizations
- Arizona State University