A robust approach to reliable real-time Kalman filtering

A complete approach to reliable, robust, and adaptive Kalman filtering is presented. It has applications in all types of navigation systems. The starting point is a measurement editing and filter divergence protection scheme based on measurement residuals and their expected statistics. Rather than simply increasing the white measurement noise variance, certain error sources which are known to be present can be included in the filter model via a Schmidt-Kalman filter, which allows certain states to be considered without being estimated. This type of filter configuration has many advantages over the usual Kalman filter such as larger region of convergence, smoother transitions between over-determined solutions, and more conservative modeling when certain states are frozen, such as during clock or altitude hold. Details are given on how this type of filter can be used with a factorized covariance. The same statistics used for filter integrity are also used to assess how well the filter is tuned to a particular dynamic environment. A reasonable adaptive process noise matrix scheme based on these statistics is presented. Specific examples of the application of these techniques in Global Positioning System receiver are given