A novel adaptive nonlinear Kalman filter scheme for DVL-aided SINS alignment in underwater vehicles

• Published in: Signal processing Vol. 209; p. 109045
• Main Authors: Jin, Kaidi, Chai, Hongzhou, Su, Chuhan, Yin, Xiao, Xiang, Minzhi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2023
• Subjects: Adaptive Kalman filter; DVL/SINS integration; Initial alignment; Nonlinear error model; Unmanned underwater vehicle; DVL/SINS integration; Nonlinear error model; Adaptive Kalman filter; Unmanned underwater vehicle; Initial alignment
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2023.109045

•A modified nonlinear SINS error model is derived in detail.•The nonlinear DVL-aided SINS alignment filter model is constructed based on the proposed SINS error model.•The noise amplification problem of applying sage-husa adaptive Kalman filter (SHAKF) in nonlinear alignment is analyzed.•A novel arctangent fading memorial factor is introduced to dynamically adjust the weight of current measurements during filtering.•100 Monto carlo simulations and unmanned surface vehicle experiments are performed to verify the performance of proposed alignment scheme. The rapid and accurate initial alignment of a strapdown inertial navigation system (SINS) aided by a Doppler velocity logger (DVL) still poses a significant challenge in unmanned underwater vehicle navigation. In this study, a novel adaptive nonlinear filtering alignment scheme is developed for a SINS with a large misalignment angle. In previous studies, the frame-inconsistency problem of the nonlinear SINS error model was neglected. Thus, a modified nonlinear error model of a SINS is derived here rigorously as the process model of the nonlinear Kalman filter. In addition, the noise amplification of applying Sage-Husa adaptive Kalman filter (SHAKF) in alignment is analyzed, and a novel arctangent fading memorial factor is introduced to dynamically adjust the weight of current measurements. Simulations and field experiments indicate that the proposed algorithm is efficient in underwater DVL/SINS in-motion alignment. The alignment accuracy and convergence rate of the modified model are significantly improved compared to those of the traditional model. The arctangent fading memorial SHAKF can reliably estimate the measurement noise and improve the performance of alignment.