Optimization-based INS in-motion alignment approach for underwater vehicles

Li, Wanli; Tang, Kanghua; Lu, Liangqing; Wu, Youlong

doi:10.1016/j.ijleo.2013.01.069

Cited by 49 publications

(20 citation statements)

References 9 publications

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“…Since the complicated are contained in the outputs of the inertial sensors, a coarse alignment method based on reconstructed observation vectors [5] were presented to solve the problem. In order to cope with any large angular motions, the optimization-based alignment (OBA) method has been proposed [13]- [15]. The OBA method equivalently transform the INS attitude alignment into a continuous attitude determination problem.…”

Section: Introductionmentioning

confidence: 99%

Odometer Aided SINS in-Motion Alignment Method Based on Backtracking Scheme for Large Misalignment Angles

et al. 2020

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In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodeticframe observations is one of the toughest challenges for odometer aided navigation system. It is difficult to obtain an accurate attitude within a short time using coarse alignment under intense maneuvering condition, so one of the main obstacles during in-motion alignment for SINS/OD is to estimate an accurate attitude during fine alignment process under large azimuth misalignment angle. However, most current in-motion alignment methods were based on linear error model. In this paper, an OD aided in-motion alignment method based on backtracking scheme for large misalignment angles is proposed. Three improvements were made as the following: (1) A continuous nonlinear 16-dimensional (16D) SINS/OD error model is derived. (2) We proposed a backtracking-based Extended Kalman filter (EKF) and (3) a strict reverse navigation method is utilized in the backward process. Experimental results illustrated that the proposed method can autonomously improve the estimated attitude accuracy under the condition of large misalignment angle. INDEX TERMS In-motion alignment, large misalignment angles, strapdown inertial navigation system (SINS), odometer (OD), backtracking, extended Kalman filter (EKF).

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Section: Introductionmentioning

confidence: 99%

Odometer Aided SINS in-Motion Alignment Method Based on Backtracking Scheme for Large Misalignment Angles

et al. 2020

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“…However, in these methods, a long alignment time (more than 300 seconds) is required to ensure sufficient random noise smoothing and time intervals between two vectors to avoid collinearity and ensure solution accuracy with the dual‐vector method. The second method is attitude‐optimization‐based initial alignment, which transforms the initial alignment problem into a continuous attitude determination problem by using infinite vector observations (Ben et al., 2011; Chang, Li, & Chen, 2015; Kang, Fang, & Wang, 2013; Kang, Ye, & Song, 2014; Li, Tang, Lu, & Wu, 2013; Wu & Pan, 2013; Wu, Wu, Hu, & Hu, 2011; Zhou, Qin, Zhang, & Cheng, 2012). All these methods listed in these papers are based on the decomposition of the attitude matrix into earth motion, inertial rate, and alignment matrix, but have different vector observation constructions and alignment matrix calculation procedures.…”

Section: Introductionmentioning

confidence: 99%

“…For underwater alignment, the vector observation is usually constructed based on the velocity integration formula in the body frame because of the adoption of the Doppler Velocity Log (DVL), which is a common auxiliary tool for underwater navigation and provides velocity measurement in the body frame. The least‐squares approach (Kang et al., 2014; Li et al., 2013) and the Kalman filtering approach (Zhou et al., 2012) are used to estimate the alignment attitude matrix. In Zhou et al.…”

Section: Introductionmentioning

confidence: 99%

A novel in‐motion alignment method for underwater SINS using a state‐dependent Lie group filter

Pei

Ning

et al. 2020

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The in‐motion alignment for the underwater strapdown inertial navigation system is still a challenging problem due to various disturbances in the underwater environment. In this paper, a novel in‐motion alignment method, based on the Lie group representation, is developed. In this method, the process model is rewritten using the Lie group of the constant attitude matrix between two inertial frames as the state. An exact linear measurement model is constructed by analyzing the effect of the sensor errors in calculating the velocity vector. Next, the state‐dependent Lie group filter is designed basing on accurate derivation expressions for the covariance matrices of state‐dependent noises. The simulation and experiment results demonstrate that the proposed method can achieve better alignment accuracy and time than the existing method. The accuracy improves by 70% with the quaternion Kalman filter.

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“…Analytic method [13,14] and the optimization-based alignment (OBA) method [15,16] are the most common means for coarse alignment. Generally, the results of coarse alignment still have significant differences relative to the true value, and subsequent fine alignment stage intends to further enhance the accuracy of the coarse alignment by implementing state estimation methods [17].…”

Section: Introductionmentioning

confidence: 99%

An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer

et al. 2019

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The strap-down inertial navigation system (SINS) is a commonly used sensor for autonomous underground navigation, which can be used for shearer positioning under a coal mine. During the process of initial alignment, inaccurate or time-varying noise covariance matrices will significantly degrade the accuracy of the initial alignment of the shearer. To overcome the performance degradation of the existing initial alignment algorithm under complex underground environment, a novel adaptive filtering algorithm is proposed by the integration of the strong tracking Kalman filter and the sequential filter for the initial alignment of the shearer with complex underground environment. Compared with the traditional multiple fading factor strong tracking Kalman filter (MSTKF) method, the proposed MSTSKF algorithm integrates the advantage of strong tracking Kalman filter and sequential filter, and multiple fading factor and forgetting factor for east and north velocity measurement are designed in the algorithm, respectively, which can effectively weaken the coupling relationship between the different states and increase strong robustness against process uncertainties. The simulation and experiment results show that the proposed MSTSKF method has better initial alignment accuracy and robustness than existing strong tracking Kalman filter algorithm.

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Optimization-based INS in-motion alignment approach for underwater vehicles

Cited by 49 publications

References 9 publications

Odometer Aided SINS in-Motion Alignment Method Based on Backtracking Scheme for Large Misalignment Angles

Odometer Aided SINS in-Motion Alignment Method Based on Backtracking Scheme for Large Misalignment Angles

A novel in‐motion alignment method for underwater SINS using a state‐dependent Lie group filter

An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer

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