Wireless indoor tracking network based on Kalman filters with an application to monitoring dairy cows

We propose an algorithm for estimating positions of devices in a sensor network using Kalman filtering techniques. The specific area of application is monitoring the movements of cows in a barn. The algorithm consists of two filters. The first filter enhances the signal-to-noise ratio of the observed signal strengths and gives interpolated values at specific timestamps. Information from the first filter is transferred to the second filter which estimates the positions. Methods for estimating the parameters of the filters are given and these provide a straightforward calibration of the system.