A Novel Monitoring Navigation Method for Cold Atom Interference Gyroscope

Zhang, Lin; Gao, Wei; Li, Qian; Li, Runbing; Yao, Zhan-Wei; Lu, Si-Bin

doi:10.3390/s19020222

Cited by 15 publications

(7 citation statements)

References 41 publications

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“…Then from Eq.7 and Eq.9, we can obtain the derivatives of angular velocity and angular velocity increment: (3) (0) = θ (3)…”

Section: A 3 Sub-sample 3rd Order Attitude Error Compensation Algorithmmentioning

confidence: 99%

“…into Eq.6 and discarding four and above orders cross multiplications because this section only considers three order terms, Eq.6 can be expressed aṡ (12) In order to make the derivation process more concise and clear, we make the following definition A = θ × χ + θ ×χ B = θ × χ + 2θ ×χ + θ ×χ C = θ (3) × χ + 3θ ×χ + 3θ ×χ D = 4θ (3) ×χ + 6θ ×χ E = 10θ (3) ×χ (13) Then the derivative of each order for Eq.12 can be indicated in (14), as shown at the top of the next. 3) × E= 7E ×χ + 14 3 θ (3) × E φ (10) = φ (11) = φ (12)…”

Section: A 3 Sub-sample 3rd Order Attitude Error Compensation Algorithmmentioning

confidence: 99%

“…4B = θ (3) × B × χ + 60 24 θ (3) × A ×χ + 60 24 θ (3) The improved high-precision attitude compensation algorithm proposed in this paper is compared with the traditional algorithm. Fig.1 shows the attitude drift error of these two algorithms.…”

Section: Experimental Verificationmentioning

confidence: 99%

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An Improved Attitude Compensation Algorithm in High Dynamic Environment

et al. 2020

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With the advent of high-precision gyro laboratory prototypes and the widespread use of gyros in highdynamic fields, the requirements for navigation calculations will definitely increase. The traditional cone error compensation algorithm is very effective in pure cone motion environment with small half cone angle. However not only the triple cross product term of the noncommutativity error is ignored but also the cross-product terms of the angular increment are simplified in this algorithm for convenience. Thus, the attitude error is extremely huge when the half cone angle is large or in high dynamic environments. Aiming at the above problems, this paper proposes an improved high-precision attitude compensation algorithm based on Taylor series expansion. In this improved algorithm, the neglected and simplified items indicated above are both considered carefully. On this basis, the compensation coefficients are given after deducing in detail. Our algorithm does not need to know the coefficient model in advance, so it is more practical and has advantages in deriving high-order algorithms or sculling compensation algorithms. In order to verify the performance of the algorithm, pure coning and high dynamic environment simulations are performed. And the results show that the improved attitude compensation algorithm can obtain higher precision, which proving its feasibility and effectiveness.

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“…Then from Eq.7 and Eq.9, we can obtain the derivatives of angular velocity and angular velocity increment: (3) (0) = θ (3)…”

Section: A 3 Sub-sample 3rd Order Attitude Error Compensation Algorithmmentioning

confidence: 99%

Section: A 3 Sub-sample 3rd Order Attitude Error Compensation Algorithmmentioning

confidence: 99%

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An Improved Attitude Compensation Algorithm in High Dynamic Environment

et al. 2020

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“…However, its functional scope is limited to the determination of the safe maneuver of the object to the most dangerous object encountered, followed by its simulation in an accelerated time scale [1,2,3]. Modern navigation systems aim to use computer decision support systems that take many factors into account [4,5,6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of Safe Trajectory in a Game Environment on Inaccuracy of Radar Data in Autonomous Navigation

Lisowski

2019

Sensors

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This article provides an analysis of the autonomous navigation of marine objects, such as ships, offshore vessels and unmanned vehicles, and an analysis of the accuracy of safe control in game conditions for the cooperation of objects during maneuvering decisions. A method for determining safe object strategies based on a cooperative multi-person positional modeling game is presented. The method was used to formulate a measure of the sensitivity of safe control in the form of a relative change in the payment of the final game; to determine the final deviation of the safe trajectory from the set trajectory of the autonomous vehicle movement; and to calculate the accuracy of information in terms of evaluating the state of the control process. The sensitivity of safe control was considered in terms of both the degree of the inaccuracy of radar information and changes in the kinematics and dynamics of the object itself. As a result of the simulation studies of the positional game algorithm, which used an example of a real situation at sea of passing one's own object with nine other encountered objects, the sensitivity characteristics of safe trajectories under conditions of both good and restricted visibility at sea are presented.

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“…Without an external reference, such as GNSS, the vehicle must rely on proprioceptive information obtained through a compass, a Doppler Velocity Logger (DVL) or an Inertial Navigation System (INS) [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Positioning for Multi-AUVs Based on Factor Graph and Maximum Correntropy

et al. 2019

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Cooperative positioning (CP) is considered as a promising positioning method for multiple autonomous underwater vehicles (multi-AUVs), because CP is characterized by low cost and high precision. In this research, a novel autonomous underwater vehicle (AUV) CP algorithm is proposed to enhance the global localization accuracy of the follower AUV. However, in traditional CP algorithm, the positioning error is large under the condition that the outlier data exists in the observation, which happens commonly. So in this research, a novel CP algorithm based on the factor graph and maximum correntropy(FGMC) for AUV is proposed to enhance the global localization accuracy of the AUV. Different from the traditional algorithms, this presented FGMC-based CP algorithm implements mathematically the Bayes filter by converting the global function estimation problem into the local one. And furthermore, the maximum correntropy is used as the cost function in the factor graph to estimation problem, this can reduce the influence of outliers on positioning accuracy. FGMC based cooperative positioning algorithm is established to mathematically implement the Bayes filter by converting the global function estimation problem into local function estimation problem. Furthermore, the maximum correntropy is used as the cost function in the factor graph to estimate the variables. To demonstrate and verify the proposed algorithm, simulation and real tests in different scenarios are performed in this research. Compared with the traditional CP algorithms, the positioning error of the proposed FGMC cooperative positioning algorithm is obviously smaller than that of the other algorithms.

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A Novel Monitoring Navigation Method for Cold Atom Interference Gyroscope

Cited by 15 publications

References 41 publications

An Improved Attitude Compensation Algorithm in High Dynamic Environment

An Improved Attitude Compensation Algorithm in High Dynamic Environment

Sensitivity of Safe Trajectory in a Game Environment on Inaccuracy of Radar Data in Autonomous Navigation

Cooperative Positioning for Multi-AUVs Based on Factor Graph and Maximum Correntropy

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