Distributed estimation and control

In this lecture, we will take a look at the fundamentals of distributed estimation. We will consider a random variable being observed by $N$ sensors. Under the assumptions of Gaussian noises and linear measurements, we will derive the weighted covariance combination of estimators. We will then touch upon the issues of distributed static sensor fusion and estimation of a dynamic random variable. Towards the end, we will look at the problem of dynamic sensor fusion, i.e., distributing a Kalman filter so that multiple sensors can estimate a dynamic random variable.