Navigational data imputation with GPS pinning in compositional Kalman filter for IoT systems

Herewith the efficiency of various configurations of employing Kalman filter algorithm for on-the-fly pre-processing of the sensory network originated data streams in the Internet of Things (IoT) systems is investigated. Contextual grouping of the data streams for pre-processing by specialized Kalman filter units is found to be able to satisfy the logistics of IoT system operations. It is demonstrated that interconnection of the elementary Kalman filters into an organized network, the compositional Kalman filter, allows to take advantage of the redundancy of data streams to accomplish IoT pre-processing of the raw data. This includes intermittent data imputation, missing data replacement, lost data recovery, as well as error events detection and correction. Demonstrated is the efficiency of the suggested compositional designs of elementary Kalman filter networks for the purpose of data pre-processing in IoT systems.