Model-Aided Localization and Navigation for Underwater Gliders Using Single-Beacon Travel-Time Differences

Sun, Jie; Hu, Feng; Jin, Wenming; Wang, Jin; Wang, Xu; Luo, Yeteng; Yu, Jian; Zhang, Aiqun

doi:10.3390/s20030893

Cited by 13 publications

(5 citation statements)

References 57 publications

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“…A new EKF method including Inertial Measurement Unit (IMU) bias estimation is applied in [10]. The literature in [11] fuses the Rauch-Tung-Striebel smoother into EKF, and a linear time-varying single-beacon navigation model is used to enhance KF performance in [12]. In addition to filtering estimation methods, there are many other methods that can be used for single-beacon localization, such as modified LS [13,14], Cayley-Menger determinant configuration [15], the second-order time difference of arrival model [16], the phase difference coordinate solution equation [17], and virtual LBL [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Liu

et al. 2022

Remote Sensing

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Singe-beacon localization technology can help Autonomous Underwater Vehicles (AUVs) to obtain precise positions by deploying only one beacon. It is considered as a promising way, benefiting from saving much time and labor compared with traditional Long-Baseline Localization (LBL). A typical single-beacon localization scheme contains two essential questions: the initial observability problem and long-endurance trajectory tracking problem. Aiming at these core problems, a comprehensive solution for single-beacon localization is described in this paper. An multi-hypothesis initial position discriminant method is proposed firstly, which helps to achieve accurate initial location based on observability analysis. Then, an Adaptive Network Fuzzy Inference System (ANFIS)-improved Extended Kalman Filter (EKF) method is proposed, in which single-beacon measuring information is fused with off-the-shelf sensors, including DVL, Compass, etc. ANFIS-EKF can help to improve trajectory tracking precisions by restraining the heavy loss of linearization in conventional EKF. Both simulation and field tests are conducted to verify the performance of the proposed algorithms.

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Section: Introductionmentioning

confidence: 99%

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

Zhao

Liu

et al. 2022

Remote Sensing

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“…However, due to the lack of sufficient position observation, it can inhibit positioning error accumulation of the integrated navigation system to a certain extent by using the above method. The positioning error of the integrated navigation system is still divergent under long-term conditions [ 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV’s Moving Vector

Zhu

Lai

Chen

2022

Sensors

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The traditional leader-follower Unmanned Aerial Vehicle (UAV) formation cooperative positioning (CP) algorithm, based on relative ranging, requires at least four leader UAV positions to be known accurately, using relative distance with leader UAVs to achieve the unknown position follower UAV’s high-precision positioning. When the number of the known position leader UAVs is limited, the traditional CP algorithm is not applicable. Aiming at the minimum cooperative unit, which consists of a known position leader UAV and an unknown position follower UAV, this paper proposes a CP method based on the follower UAV’s moving vector. Considering the follower UAV can only acquire the single distance with the leader UAV at each distance-sampling period, it is difficult to determine the follower UAV’s spatial location. The follower UAV’s moving vector is used to construct position observation of the follower UAV’s inertial navigation system (INS). High-precision positioning is achieved by combining the follower UAV’s moving vector. In the process of CP, the leader UAV obtains a high-precision position by an INS/Global Positioning System (GPS) loosely integrated navigation system and transmits its position information to the follower UAV. Based on accurate modeling of the follower UAV’s INS, the position, velocity and heading observation equation of the follower UAV’s INS are constructed. The improved extended Kalman filtering is designed to estimate the state vector to improve the follower UAV’s positioning accuracy. In addition, considering that the datalink system based on radio signals may be interfered with by the external environment, it is difficult for the follower UAV to obtain relative distance information from the leader UAV in real time. In this paper, the availability of the relative distance information is judged by a two-state Markov chain. Finally, a real flight test is conducted to validate the performance of the proposed algorithm.

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“…Underwater gliders need several subsystems to operate correctly. Their operations include the monitoring of internal variables for glider safety, communication with the command center, GPS system, buoyancy system, energy storage, and oceanographic data collection [15][16][17]. There are several states in the process of launching and capturing of a submarine glider: (a) The surface mode in which the systems are tested.…”

Section: Introductionmentioning

confidence: 99%

Development of a Datalogger for Submarine Glider: Integration of Fault-Tolerant Software Layers

León-Gordillo

Rodríguez-Olivares

Barriga-Rodríguez

et al. 2021

JMSE

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Submarine gliders are specialized systems used in applications such as environmental monitoring of marine fauna, in the oil industry, among others. The glider launch and capture is a costly process that requires substantial technological and human resources, so the orderly and error-free storage of data is of fundamental importance due to the subsequent analysis. The amount of information being obtained from the seabed is increasing, this leads to the need to develop robust and low-cost ad-hocsystems for this type of application. The challenge is the integration of the different software layers in the storage system because the monitored variables must be ordered according to different glider operations such as calibration data update and navigation. Additionally, to avoid data corruption in the memory chip, error control coding must be used. The goal of this paper is to present a novel design of different layers of software integrated into a datalogger: reception, error control, and storage logic for the different glider operations. The design of the datalogger is based on a NAND flash memory chip and an MSP430 microcontroller. To correct bit-flipping errors, a BCH code that corrects 4 errors for every 255 bits is implemented into the microcontroller. The design and evaluation are performed for different glider operations, and for different lengths and correction capabilities of the BCH module. A test to calculate the storage time has been carried out. This test shows that in the case of 256 bytes per sample, at 30 samples per minute, and 1 GB of storage capacity, it is possible to collect data from the glider sensors for 84 days. The results obtained show that our device is a useful option for storing underwater sensor data due to its real-time storage, power consumption, small size, easy integration, and its reliability, where the bit error rate BER is of 2.4 ×10−11.

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Model-Aided Localization and Navigation for Underwater Gliders Using Single-Beacon Travel-Time Differences

Cited by 13 publications

References 57 publications

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

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

Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV’s Moving Vector

Development of a Datalogger for Submarine Glider: Integration of Fault-Tolerant Software Layers

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