A Novel INS and Doppler Sensors Calibration Method for Long Range Underwater Vehicle Navigation

Abstract

Since the drifts of Inertial Navigation System (INS) solutions are inevitable and also grow over time, a Doppler Velocity Log (DVL) is used to aid the INS to restrain its error growth. Therefore, INS/DVL integration is a common approach for Autonomous Underwater Vehicle (AUV) navigation. The parameters including the scale factor of DVL and misalignments between INS and DVL are key factors which limit the accuracy of the INS/DVL integration. In this paper, a novel parameter calibration method is proposed. An iterative implementation of the method is designed to reduce the error caused by INS initial alignment. Furthermore, a simplified INS/DVL integration scheme is employed. The proposed method is evaluated with both river trial and sea trial data sets. Using 0.03°/h(1σ) ring laser gyroscopes, 5 × 10−5 g(1σ) quartz accelerometers and DVL with accuracy 0.5% V ± 0.5 cm/s, INS/DVL integrated navigation can reach an accuracy of about 1‰ of distance travelled (CEP) in a river trial and 2‰ of distance travelled (CEP) in a sea trial.

Auto-Correction of 3D-Orientation of IMUs on Electric Bicycles

“…In general, IMUs can be readily calibrated with a Kalman filter. Nevertheless, in some specific cases, as seen in [22], the occurring motion data is not sufficient for proper convergence of the filter states. Therefore, the existing filter approaches are not sufficient for the bicycle use case, too, as it has similarities to the task presented in [22].…”

“…Especially in cycling activities in flat areas, the roll and pitch angle of a bicycle exhibits only small variance regarding orientation changes, which will lead to poor performances when using the approaches in [16][17][18][19][20] To evade the necessity of measurement data with different orientations, further sensors (e.g. high-grade GPS data) could be introduced to the Kalman Filter design as shown in [22]. Figure 2.…”

“…Nevertheless, in some specific cases, as seen in [22], the occurring motion data is not sufficient for proper convergence of the filter states. Therefore, the existing filter approaches are not sufficient for the bicycle use case, too, as it has similarities to the task presented in [22]. Especially in cycling activities in flat areas, the roll and pitch angle of a bicycle exhibits only small variance regarding orientation changes, which will lead to poor performances when using the approaches in [16][17][18][19][20] To evade the necessity of measurement data with different orientations, further sensors (e.g., high-grade GPS data) could be introduced to the Kalman Filter design as shown in [22].…”

Calibração De Erros Sistemáticos Em Acelerômetros Mems

“…Diversos métodos de calibração têm sido desenvolvidos, sendo alguns voltados para aplicações específicas e integração com outros sensores. Como exemplo, pode-se citar a calibração voltada à integração de INS com GPS (Hol, 2011), câmeras (Bender et al, 2013), veículos autônomos submarinos (Tang et al, 2013) e veículos aéreos não tripulados do tipo drone (Henriques, 2011). Outros métodos de calibração e modelagem são mais generalizados, tendo como foco o tipo de erro a ser calibrado.…”

A fast in-situ SINS and Doppler sensor calibration algorithm for underwater vehicle navigation

“…The common method is to transfer the position or velocity information gathered from an aiding source such as Global Navigation Satellite System (GNSS) into the SINS's body frame according to the attitude. After voyaging a certain distance, an enough signal noise ratio (SNR) between the travel distance and the position error could be obtained [8]. And the mounting angles between the SINS and DVL could be estimated through a Kalman filter [9] or a least square error estimator [10].…”