Adaptive Federated IMM Filter for AUV Integrated Navigation Systems

Lyu, Weiwei; Cheng, Xianghong; Wang, Jinling

doi:10.3390/s20236806

Cited by 15 publications

(9 citation statements)

References 41 publications

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“…As far as a comparison of the dataset analysis is concerned, the reduction of the navigation drift in the second mission with respect to the first one, despite the greater length of the route, can be related to the surrounding environment properties (see Figs. 26 and 28 and Table V). As a matter of fact, the presence of a more varied and textured environment is a significant advantage for VO/AO.…”

Section: Analysis Of the Resultsmentioning

confidence: 87%

“…As far as this work is concerned, the sequential (iterated) [22] and both the standard and the reduced UKFs have been subject of investigation. When compared with centralized solutions, decentralized methods significantly reduce the computational effort and increase the fault-tolerant capability of the overall navigation system [23]- [26]. Within the decentralized filtering realm, the federated filters introduced by Carlson [23] and [24] represent one of the most famous solutions.…”

Section: B Related Workmentioning

confidence: 99%

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Evaluation of UKF-Based Fusion Strategies for Autonomous Underwater Vehicles Multisensor Navigation

Bucci

Franchi

Ridolfi

et al. 2023

IEEE J. Oceanic Eng.

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In the underwater domain, guaranteeing accurate navigation for an autonomous underwater vehicle (AUV) is a complex but fundamental task to be achieved. As a matter of fact, only by ensuring a correct AUV localization, it is possible to accomplish surveillance, monitoring, and inspection missions. Most of the navigation filters for AUVs are based on Bayesian estimators, such as the Kalman filter, the extended Kalman filter (EKF), or the unscented Kalman filter (UKF), and employ different instruments, including the Doppler velocity log to perform the localization task. Recently, the use of payload sensors, such as cameras or forward-looking SONARs, in navigation-aiding has arisen as an interesting research field in the attempt to reduce the localization error drift. Such sensors, if used simultaneously, can provide multiple observations, which can be combined in a Kalman filtering framework to increase navigation robustness against noise sources. Navigation techniques that employ multiple devices can provide a high improvement of the estimation quality, but they can also cause an increase in terms of computational load. Consequently, strategies that can represent a tradeoff between these two conflicting goals have to be investigated. In this contribution, two different frameworks have been implemented and compared: on the one hand, a centralized iterative UKF-based navigation approach and, on the other hand, a sensor fusion framework with parallel local UKFs. The sequential (or iterated) UKF, where the correction step is iteratively performed for each available measurement, belongs to the first class of filters. The federated and the consensus-based decentralized UKFs can be categorized as the second class and they differ in the employed fusion strategy. Experimental navigation data obtained during sea trials performed at Vulcano Island, Messina, Italy has been used for offline validation. The results analysis focuses on both the navigation quality and the filter robustness against the reduction of the available measurements.

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Section: Analysis Of the Resultsmentioning

confidence: 87%

Section: B Related Workmentioning

confidence: 99%

Evaluation of UKF-Based Fusion Strategies for Autonomous Underwater Vehicles Multisensor Navigation

Bucci

Franchi

Ridolfi

et al. 2023

IEEE J. Oceanic Eng.

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“…The third is that the navigation system model of aerospace vehicle is inconsistent with the filter model due to large dynamic flight, which leads to the decline of filter estimation accuracy. In recent years, researchers have solved the problem that the accuracy of integrated navigation system decreases due to the inaccurate error model through the research of the adaptive filter [9,10] and IMM filter algorithm [11]. According to the accurate modelling of aerospace vehicle navigation system, it can provide high-precision state quantity and measurement quantity for the subsequent filtering algorithm and then improve the accuracy of navigation parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

Integrated Navigation Method of Aerospace Vehicle Based on Rank Statistics

Kang

Xiong

Zhang

2023

International Journal of Aerospace Engineering

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The large dynamic and high-speed flight of aerospace vehicle will bring unpredictable conditions to its navigation system, resulting in that its system random noise probability distribution will no longer meet the preconditions of Gaussian distribution preset by the existing filter algorithm, thus reducing the accuracy of the navigation system. So, it is very important to propose an effective method to solve the filter problem of the navigation system in non-Gaussian distribution to improve the accuracy of the navigation system. Therefore, an integrated navigation method of aerospace vehicle based on rank statistics (LRF) has been proposed in this paper. Firstly, based on the flight characteristics of aerospace vehicles, an accurate gravity calculation model has been established to improve the accuracy of system modelling. Then, the state equation and measurement equation of integrated navigation system have been established. In combination with the rank filter algorithm as well as the determined weights, sampling points are calculated and nonlinearly propagated through the transition matrix to achieve an accurate estimation about the predicted values of the state quantities and measurement quantities and the covariance matrix. In turn, it simulates the probability distribution of the system state effectively. Therefore, when the system random noise probability distribution of the aerospace vehicle does not meet the Gaussian distribution due to various interference factors in the actual flight process, the algorithm can simulate the probability distribution of the actual system to the greatest extent, to improve the accuracy of the integrated navigation system and enhance the reliability of the navigation system ultimately.

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“…Additionally, the mentioned phenomenon is more common in the polar region than in the non-polar region, which can significantly affect the accuracy of the DVL-aided SINS. Aiming at solving this problem, an adaptive Kalman filter or robust Kalman filter is usually adopted [ 13 , 14 , 15 , 16 , 17 ]. The main idea of the adaptive Kalman filter is to adjust the measurement noise covariance matrix or the system noise covariance matrix, which can further modify the Kalman filter gain and improve filter performance.…”

Section: Introductionmentioning

confidence: 99%

A Polar Robust Kalman Filter Algorithm for DVL-Aided SINSs Based on the Ellipsoidal Earth Model

Tian

Liang

Liao

et al. 2022

Sensors

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Autonomous underwater vehicles (AUVs) play an increasingly essential role in the field of polar ocean exploration, and the Doppler velocity log (DVL)-aided strapdown inertial navigation system (SINS) is widely used for it. Due to the rapid convergence of the meridians, traditional inertial navigation mechanisms fail in the polar region. To tackle this problem, a transverse inertial navigation mechanism based on the earth ellipsoidal model is designed in this paper. Influenced by the harsh environment of the polar regions, unknown and time-varying outlier noise appears in the output of DVL, which makes the performance of the standard Kalman filter degrade. To address this issue, a robust Kalman filter algorithm based on Mahalanobis distance is used to adaptively estimate measurement noise covariance; thus, the Kalman filter gain can be modified to weight the measurement. A trial ship experiment and semi-physical simulation experiment were carried out to verify the effectiveness of the proposed algorithm. The results demonstrate that the proposed algorithm can effectively resist the influence of DVL outliers and improve positioning accuracy.

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Adaptive Federated IMM Filter for AUV Integrated Navigation Systems

Cited by 15 publications

References 41 publications

Evaluation of UKF-Based Fusion Strategies for Autonomous Underwater Vehicles Multisensor Navigation

Evaluation of UKF-Based Fusion Strategies for Autonomous Underwater Vehicles Multisensor Navigation

Integrated Navigation Method of Aerospace Vehicle Based on Rank Statistics

A Polar Robust Kalman Filter Algorithm for DVL-Aided SINSs Based on the Ellipsoidal Earth Model

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