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An unconventional GPS and multiple low-cost IMU integration strategy with individual model for systematic errors and measurements

Conventionally, the Kalman filter on the basis of integration mechanization of GPS-aided inertial integrated navigation system has been commonly built up using error states and error measurements. Wang et al. [2015] and Qian et al. [2015, 2016] developed an unconventional KF that directly estimates navigational parameters instead of the error states, in which a kinematic trajectory model as the KF system model was deployed and measurement updates for all sensor data inclusive of the ones from IMUs were directly performed. This research applies the above mentioned novel multisensor integration strategy to integrate GPS receiver and multiple low-cost IMUs so that the systematic errors and measurements of these IMUs can be individually modeled in the navigation Kalman filter, instead of being a group of the commonly shared states for all of the IMUs. Experiments and simulations were tested to show the practicability of the proposed integrated navigation strategy.