Mario B. Ignagni

Separate bias Kalman estimator with bias state noise

A modified decoupled Kalman estimator suitable for use when the bias vector varies as a random-walk process is defined and demonstrated in a practical application consisting of the calibration of a strapdown inertial navigation system. The estimation system accuracy associated with the modified estimator is shown to be essentially the same as that of the generalized partitioned Kalman estimator. Considering that the sensor error random rates assumed in the example are on the order of 5 to 10 times greater than normally associated with contemporary strapdown systems, it may be inferred that inertial navigation systems possessing more typical sensor error random growth characteristics should be amenable to a decoupled estimator approach in a broad spectrum of aided-navigation system applications. This should also be true in a variety of other applications in which the bias vector experiences only limited random variation. >

An alternate derivation and extension of Friendland's two-stage Kalman estimator

An alternate simplified derivation of Friedlands two-stage Kalman estimator is given for a somewhat more general class of problems than considered by Friedland. Friedlands result is also extended to encompass two variations on the basic idea which are of practical interest.

Optimal and suboptimal separate-bias Kalman estimators for a stochastic bias

Addresses two issues concerning the separate-bias Kalman estimator. The first of these issues deals with the derivation of the optimal estimator for the general case in which the bias vector is stochastic in nature, and the second issue deals with defining a suitable suboptimal realization of the generalized estimator.