Gong Hang

One-state and twice Kalman filter timescale algorithms

We first propose a one-state Kalman filter time scale algorithm, nand derive the analytical expressions of the weights and the predictions. nBased on the analytical expressions, we introduce the virtual Kalman nsampling time. The theoretical analyses and the simulations both validate nthat we can optimize the frequency stability on any one of the certain nobservation intervals of the time scale by means of choosing a certain nvirtual Kalman sampling time. Then, based on this algorithm, we propose na twice Kalman filter time scale algorithm, and describe the principle nof the algorithm. The forming time scale only involves walk random nfrequency modulation noise (RWFM), and does not involve white frequency nmodulation noise (WFM). The weights are in inverse proportion to the nintense of RWFM. The short-term and middle-term frequency stability nof the time scale is higher.