ANFIS-EKF-Based Single-Beacon Localization Algorithm for AUV

Zhao, Wanlong; Zhao, Huifeng; Liu, Gongliang; Zhang, Guoyao

doi:10.3390/rs14205281

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…Because the changing process of bias and drift are modeled with first-order Gauss-Markov process [13], the bias can be written in the form of (21), where Tc is the correlation time, dt is the sample period of the filter and w b is the zero mean Gaussian noise of bias.…”

Section: The System Modelmentioning

confidence: 99%

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Tightly Coupled INS/APS Passive Single Beacon Navigation

Zou

Wang

et al. 2023

Remote Sensing

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Unlike aerial or terrestrial navigation, the global navigation satellite system (GNSS) is not available underwater. This is a big challenge for underwater navigation. The inertial navigation system (INS) aided by the single-beacon acoustic positioning system (APS) provides one solution, but the long-range case is limited by low-SNR conditions. Inspired by passive synthetic aperture detection, we proposed a new tightly coupled navigation algorithm based on spatial synthesis and one-way-travel-time (OWTT) range measurement. We design two estimators: the DOA/range estimator using the model-based method and the tightly coupled INS/APS navigation estimator. Based on the improved UKF, all information is combined. Simulation is carried out in MATLAB. Compared with range-only tightly coupled INS/APS navigation, synthetic long baseline (SLBL) algorithm and Doppler velocity logger (DVL) aided centralized extended Kalman filter (CEKF) based single beacon INS/OWTT navigation, the proposed method’s performance is proven. The main contributions of this work are: (1). Propose a new architecture of underwater integrated navigation; (2). Apply the passive acoustic detecting method in the navigation to improve accuracy. (3). Apply the tightly coupled method to improve availability.

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Section: The System Modelmentioning

confidence: 99%

“…However, the effective range limits its application [20]. Zhao uses the adaptive network fuzzy inference system and extended Kalman filter in single-beacon navigation [21].…”

Section: Introductionmentioning

confidence: 99%

Tightly Coupled INS/APS Passive Single Beacon Navigation

Zou

Wang

et al. 2023

Remote Sensing

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“…Due to its simple structure and considerable scalability, the single acoustic-beacon range-only navigation has attained increasing attention (Jakuba et al, 2021;Rypkema et al, 2018;Zhao et al, 2022). The One-Way Travel Time (OWTT)-based passive inverted USBL (piUSBL) system estimates the OWTT slant range and azimuth by acquiring broadcast signals from a time-synchronized acoustic-beacon (Wang, et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

Underwater inertial error rectification with limited acoustic observations

Tang,

He,

et al. 2024

Satell Navig

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Underwater inertial navigation is particularly difficult for the long-durance operations as many navigation systems such global satellite navigation systems are unavailable. The acoustic signal is a marvelous choice for underwater inertial error rectification due to its underwater penetration capability. However, the traditional Acoustic Positioning Systems (APS) are expensive and incapable of positioning with limited acoustic observations. Two novel underwater inertial error rectification algorithms with limited acoustic observations are proposed. The first one is the single acoustic-beacon Range-only Matching Aided Navigation (RMAN) method, which is inspired by matching navigation without reference maps and presented for the first time. The second is the improved single acoustic-beacon Virtual Long Baseline (VLBL) method, which considers the impact of indicated relative position increments on virtual beacon reconstruction. Both RMAN and improved VLBL are further developed when multi acoustic-beacons are available, named mAB-RMAN and mAB-VLBL. The comprehensive simulations and field investigations were conducted. The results demonstrated that the proposed methods achieved excellent accuracy and stability compared to the baseline, specifically, the mAB-RMAN and mAB-VLBL can reduce the inertial error by more than 90% and 98% when using single and double acoustic-beacons, respectively. These proposed techniques will provide new perspectives for underwater positioning, navigation, and timing.

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“…Covering 71% of the Earth's surface area while being rich in material resources such as minerals, energy and organisms [1], the oceans are extremely important for both the natural ecosystem and human society. Therefore, researchers have invested much effort in researching intelligent, stable and efficient ocean exploration technologies.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Ocean Information-Enabled AUV Motion Planning Based on Reinforcement Learning

Li,

He,

et al. 2023

Remote Sensing

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Motion planning based on the reinforcement learning algorithms of the autonomous underwater vehicle (AUV) has shown great potential. Motion planning algorithms are primarily utilized for path planning and trajectory-tracking. However, prior studies have been confronted with some limitations. The time-varying ocean current affects algorithmic sampling and AUV motion and then leads to an overestimation error during path planning. In addition, the ocean current makes it easy to fall into local optima during trajectory planning. To address these problems, this paper presents a reinforcement learning-based motion planning algorithm with comprehensive ocean information (RLBMPA-COI). First, we introduce real ocean data to construct a time-varying ocean current motion model. Then, comprehensive ocean information and AUV motion position are introduced, and the objective function is optimized in the state-action value network to reduce overestimation errors. Finally, state transfer and reward functions are designed based on real ocean current data to achieve multi-objective path planning and adaptive event triggering in trajectorytracking to improve robustness and adaptability. The numerical simulation results show that the proposed algorithm has a better path planning ability and a more robust trajectory-tracking effect than those of traditional reinforcement learning algorithms.

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ANFIS-EKF-Based Single-Beacon Localization Algorithm for AUV

Cited by 4 publications

References 29 publications

Tightly Coupled INS/APS Passive Single Beacon Navigation

Tightly Coupled INS/APS Passive Single Beacon Navigation

Underwater inertial error rectification with limited acoustic observations

Comprehensive Ocean Information-Enabled AUV Motion Planning Based on Reinforcement Learning

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