A track algorithm for the deep space environment using Bierman's factorization of the Kalman filter

A combination of environmental factors is shown to be the driving force behind design requirements for a track filter in deep space. Very long ranges, long radar signal integration times, and large a priori error compared to sensor error are the major considerations which affect the filter design. The track filter in the Perimeter Acquisition radar (also known as the Radar Principal Cartesian Coordinates (RPCC) Kalman filter) is adapted to the deep space environment by using Bierman's (1976) factorization technique. A comparison of the factorized filter against the RPCC Kalman and standard Kalman filters illustrates the unique numerical and computational features of this new track filter.