Parallel Implementation of the Extended Square-Root Covariance Filter for Tracking Applications

parallel implementations of the extended square-root covariance filter(ESRCF) for tracking applications are developed in this paper. The decoupling technique and special properties in the tracking Kalman filter (KF) are explored to reduce computational requirements and to increase parallelism. The application of the decoupling technique to the ESRCF results in the time and measurement updates of m decoupled (n/m) - dimensional matrices instead of 1 coupled n-dimensional matrix, where m denotes the tracking dimension and n denotes the number of state elements. The updates of m decoupled matrices are found to require approximately m tuimes less processing elements and clock cycles than the updates of 1 coupled matrix. The transformation of the Kalman gain which accounts for the decoupling technique is found straightforward to implement. The sparse nature of the measurement matrix and the sparse, band nature of the transition matrix are explored to simplify matrix multiplications