Machine learning based asynchronous computational framework for generalized Kalman filter.

Although the Kalman filter algorithms are well suited to be executed on most digital systems, they become slow when applied to large-scale dynamic systems. Therefore, efficient execution of Kalman filter for the time-critical and large-scale applications is of the essence. This work aims to address this necessity by developing a novel framework to improve the performance of a generalized Kalman filter with unknown inputs (GKF-UI) using multithreaded-multicore processors and machine learning (ML) classification methods. An asynchronous execution model based on OpenMP message-passing framework is developed and integrated with a novel supervised ML-based thread classifier for the GKF-UI algorithm to enhance execution efficiency. The experimental results show that the proposed approach can achieve up to 35.5x speedup over the serial single-threaded implementations with no losses in the accuracy or changes to the generality of the filter structure. Moreover, this framework can play a significant role in realizations of computational advantages in large-scale systems as well as for the time-critical prediction applications.