Abstract
High accuracy and reliable navigation in the underwater environment is very critical for the operations of autonomous underwater vehicles (AUVs). This paper proposes an adaptive federated interacting multiple model (IMM) filter, which combines adaptive federated filter and IMM algorithm for AUV in complex underwater environments. Based on the performance of each local system, the information sharing coefficient of the adaptive federated IMM filter is adaptively determined. Meanwhile, the adaptive federated IMM filter designs different models for each local system. When the external disturbances change, the model of each local system can switch in real-time. Furthermore, an AUV integrated navigation system model is constructed, which includes the dynamic model of the system error and the measurement models of strapdown inertial navigation system/Doppler velocity log (SINS/DVL) and SINS/terrain aided navigation (SINS/TAN). The integrated navigation experiments demonstrate that the proposed filter can dramatically improve the accuracy and reliability of the integrated navigation system. Additionally, it has obvious advantages compared with the federated Kalman filter and the adaptive federated Kalman filter.
Highlights
Autonomous underwater vehicle (AUV) is an efficient underwater working platform that has been widely used for various underwater tasks in the areas of oil and gas industry, ocean mapping, archaeological exploration, military reconnaissance missions, search and rescue operations, etc. [1,2,3,4,5].Over the past few decades, autonomous underwater vehicles (AUVs) has been developed rapidly, due to its great value of application [4,5].Accurate navigation and positioning is a prerequisite for AUV to perform underwater operations, and a technical guarantee for its safe return [3,6]
It indicates that among the three filtering methods, the proposed adaptive federated interacting multiple model (IMM) filter can achieve the highest accuracy of integrated navigation, and the adaptive federated Kalman filter is second, followed by the federated Kalman filter
Each local system of the integrated navigation system includes different models, with the change of the underwater environment, and the adaptive federated IMM filter can use the most accurate mixed model to describe the current state of the local system
Summary
Accurate navigation and positioning is a prerequisite for AUV to perform underwater operations, and a technical guarantee for its safe return [3,6]. Navigation and positioning is one of the benchmark technologies to evaluate the level of development and the maturity of engineering application of AUV [1,3,4]. AUV reach the operation site accurately and return safely is still a challenging issue [4,5]. Most of AUVs adopt a strapdown inertial navigation system (SINS) as the reference navigation system [3,7]. SINS is an independent navigation system that is able to provide comprehensive navigation information, including the velocity, position, and attitude [8,9]. SINS is often aided by other navigation systems, such as Doppler velocity log (DVL) [2,7,12], magnetometer [7], Sensors 2020, 20, 6806; doi:10.3390/s20236806 www.mdpi.com/journal/sensors
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