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