Estimation fusion with data-driven communication

This paper deals with the problem of estimating the state of a discrete-time stochastic linear system based on data collected from multiple sensors with limited communication resources. For the cases of transmitting measurements and local state estimates, respectively, we design data-driven communication schemes based on a normalized innovation vector and corresponding fusion rules in the (approximate) minimum mean square error (MMSE) sense. These communication schemes can achieve a trade-off between communication costs and estimation performance. These fusion rules can allow the estimator to improve its estimate based on the fact that no transmission of data indicates a small innovation. A simulation example is provided to confirm the effectiveness of the proposed strategies.