The effect of missing data on the steady-state performance of an alpha , beta tracking filter

The Kalman filter provides a recursive least-mean-square estimate of parameters in a dynamic system. Because the initial variances of the measurements used in the estimation are uncertain in a practical situation, a tracking filter can be optimum only in steady-state. The steady-state error of a version of the Kalman filter, called the alpha , beta filter, is analyzed under the assumption that missing data may occur. The results are developed for a constant-scan-rate radar. The number of intervals between valid data is modeled as a geometric random variable with the probability of valid data as a parameter. It is shown that missing data can introduce large additional tracking error for slowly scanning radars.<>