Optimal distributed estimation fusion with compressed data

Considering communication constraints and affordable computational resources at the fusion center (e.g., in sensor networks), it is more beneficial for local sensors to send in compressed data. In this paper, a linear local compression rule is first constructed based on the full rank decomposition of the measurement matrix at each local sensor. Then an optimal distributed estimation fusion algorithm with the compressed data is proposed. It has three nice properties. Compression along time in the case of reduced-rate communication for some simpler cases and an extension to the singular measurement noise case are also discussed. Several counterexamples are provided to answer some potential questions.