Increasing the computational efficiency of discrete Kalman filters

When the additive noise vector in the discrete observation process of a system can be partitioned into uncorrelated subvectors, an iterative processing technique for updating the Kalman-filter covariance matrix can often be used to increase computational efficiency. For standard typical programming algorithms and for a typical computer, the iterative processing technique can theoretically reduce the computational requirements of the covariance updating equation by over 50 percent. In practical situations, computational savings of over 30 percent are realizable, a significant amount particularly for real-time tracking applications in high-target-density environments. Furthermore, independent of the computational advantages, the iterative processing technique is useful for track management, permitting effective utilization of priority and interrupt schemes without disturbing the Kalman-filter operation.