We proposed an adaptive H-infinity Cubature Kalman Filter (AH∞CKF) to improve the navigation accuracy of a highly manoeuvrable unmanned aerial vehicle (UAV). AH∞CKF fuses the Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) measurements. Traditional state estimation filters like extended Kalman filters (EKF) and cubature Kalman filters (CKF) assume Gaussian noises. However, their performance degrades for non-Gaussian noises and system uncertainties encountered in real-world applications. Thus, designing filters robust to noise and distribution is crucial. AH∞CKF combines H∞CKF design with an added adaptive factor to adjust the state estimation covariance matrix according to measurements by exploiting the square root method to yield more numerically stable results (SrAH∞CKF). We conducted multiple dynamically rich flight tests to validate our claims using a UAV equipped with a commercially well-known GNSS solution. Results show that the SrAH∞CKF state estimation outperforms EKF and CKF methods on average by 90% in various statistical measures.

In the conventional integrated navigation system, the statistical information of the process and measurement noises is considered constant. However, due to the changing dynamic environment and imperfect knowledge of the filter statistical information, the process and measurement covariance matrices are unknown and time-varying. In this paper, a multirate adaptive Kalman filter is proposed to improve the performance of the Error State Kalman Filter (ESKF) for a marine navigation system. The designed navigation system is composed of a strapdown inertial navigation system along with Doppler velocity log and inclinometer with different sampling rates. In the proposed filter, the conventional adaptive Kalman filter is modified by adaptively tuning the measurement covariance matrix of the auxiliary sensors that have varying sampling grates based on the innovation sequence. The performance of the proposed filter is evaluated using real measurements. Experimental results show that the average root mean square error of the position estimated by the proposed filter can be decreased by approximately 60% when compared to that of the ESKF.

In underwater navigation, the conventional Error State Kalman Filter (ESKF) is used for combining navigation data where due to first order linearization of the nonlinear equations of the dynamics and measurements, considerable error is induced in estimated error state and covariance matrices. This paper presents an underwater integrated inertial navigation system using the unscented filter as an improved nonlinear version of the Kalman filter family. The designed system consists of a strap-down inertial navigation system accompanying Doppler velocity log and depth meter. In the proposed approach, to use the nonlinear capabilities of the unscented filtering approach the integrated navigation system is implemented in a direct approach where the nonlinear total state dynamic and and measurement models are utilised without any linearization. To our knowledge, no results have been reported in the literature on the experimental evaluation of the unscented-based integrated navigation system for underwater vehicles. The performance of the designed system is studied using real measurements. The results of the lake test show that the proposed system estimates the vehicle's position more accurately compared with the conventional ESKF structure.