Progressive estimation and detection

For large-scale multi-hop wireless sensor networks, progressive estimation and detection algorithms are developed. For progressive estimation, the classical principles of best linear unbiased estimation (BLUE) and linear minimum mean square error (LMMSE) estimation are applied. The difference and similarity between the two are revealed. For progressive detection, a tradeoff between detection performance and limited decision propagation is studied. The progressive estimation and detection algorithms are distributed and performed at the application layer of wireless sensor networks, which are fully adaptive to the operations at the networking and other lower layers which can be designed for high spectral efficiency.