Multiple model adaptive estimation for inertial navigation during Mars entry

A multiple model adaptive estimation (MMAE) scheme is derived to determine both the position and the attitude of a vehicle during entry, descent and landing on Mars. MMAE uses several extended Kalman filters running in parallel, each representing a hypothesis of the actual system, to generate enhanced state and parameter estimates. The estimates of each parallel filter are combined based on the likelihood that each hypothesis is correct, which is determined from measurement residuals. The filter formulation is based on standard inertial navigation equations. The global attitude parameterization is given by a quaternion, while a generalized three-dimensional attitude representation is used to define the local attitude error. A multiplicative quaternion-error approach is used to guarantee that quaternion normalization is maintained in the filters. Three Kalman filters are used in the MMAE scheme: a 9 state filter, which includes attitude, position and velocity states, a 15 state filter, which adds gyro and accelerometer biases to the state state vector, and a 21 state filter, which adds gyro and accelerometer scale factors to the state vector. Simulation results are provided to show the effectiveness of the MMAE scheme.