Batch Sequential Estimation with Non-Uniform Measurements and Non-Stationary Noise

Abstract

Sequential estimation using the traditional discrete Kalman filter typically assumes the measurement time and state update time are coincident. This is often a poor assumption in realistic measurement scenarios where the data can be received from multiple sources at differing times. This paper develops the necessary algorithm adjustments needed for the Kalman filter to readily process measurement data that arrive at varying times and with non-stationary noise. The algorithm is applied to a relevant problem of orbit determination using one-way uplink radiometric tracking of a spacecraft (in the present case a Mars orbiter).