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Abstract
Because of the complex task environment, long working distance, and random drift of the gyro, the positioning error gradually diverges with time in the design of a strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated positioning system. The use of velocity information in the DVL system cannot completely suppress the divergence of the SINS navigation error, which will result in low positioning accuracy and instability. To address this problem, this paper proposes a SINS/DVL integrated positioning system based on a filtering gain compensation adaptive filtering technology that considers the source of error in SINS and the mechanism that influences the positioning results. In the integrated positioning system, an organic combination of a filtering gain compensation adaptive filter and a filtering gain compensation strong tracking filter is explored to fuse position information to obtain higher accuracy and a more stable positioning result. Firstly, the system selects the indirect filtering method and uses the integrated positioning error to model the navigation parameters of the system. Then, a filtering gain compensation adaptive filtering method is developed by using the filtering gain compensation algorithm based on the error statistics of the positioning parameters. The positioning parameters of the system are filtered and information on errors in the navigation parameters is obtained. Finally, integrated with the positioning parameter error information, the positioning parameters of the system are solved, and high-precision positioning results are obtained to accurately position autonomous underwater vehicles (AUVs). The simulation results show that the SINS/DVL integrated positioning method, based on the filtering gain compensation adaptive filtering technology, can effectively enhance the positioning accuracy.
Highlights
Autonomous underwater vehicles (AUVs) play an important role during the exploration, development, and utilization of ocean resources in the military and national economy [1,2,3]
In this paper, considering positioning error resources and their impact on the positioning result, we propose a strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated positioning system based on filtering gain compensation adaptive filtering technology
Based on the filtering algorithm and the SINS/DVL integrated navigation system algorithm we described in the previous section, we verify the feasibility of and validate our proposed method in the Matlab environment, and compare our proposed method with other related methods
Summary
Autonomous underwater vehicles (AUVs) play an important role during the exploration, development, and utilization of ocean resources in the military and national economy [1,2,3]. Observation of sea-bottom landforms, detection of specific objects, and submarine cable patrol [4,5,6] During these operations, AUVs must know their own location and where to go, so underwater positioning technology is critical in order for underwater vehicles to safely and effectively perform their missions. Underwater positioning methods can be classified into three groups [9,10]: dead reckoning and inertial navigation; acoustic navigation; and geophysical navigation. These positioning methods can be utilized independently or in combination to achieve integrated navigation and improve navigation accuracy. In this paper, considering positioning error resources and their impact on the positioning result, we propose a SINS/DVL integrated positioning system based on filtering gain compensation adaptive filtering technology
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