Analytic Coarse Transfer Alignment Based on Inertial Measurement Vector Matching and Real-Time Precision Evaluation

Lu, Jiazhen; Xie, Lei; Li, Baoguo

doi:10.1109/tim.2015.2502879

Cited by 32 publications

(14 citation statements)

References 12 publications

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“…If τ is too small, the information of the process model will be also lost. According to the research result of [27], the optimal range of the turning parameter is τ ∈ [2,6], which has better estimation performance and estimation accuracy. The forgetting factor ξ also adjusts the influence ofR k−1 .…”

Section: The Proposed Adaptive Cubature Kalman Filtermentioning

confidence: 99%

An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

Zhang

Liu

2019

Sensors

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Initial alignment is critical and indispensable for the inertial navigation system (INS), which determines the initial attitude matrix between the reference navigation frame and the body frame. The conventional initial alignment methods based on the Kalman-like filter require an accurate noise covariance matrix of state and measurement to guarantee the high estimation accuracy. However, in a real-life practical environment, the uncertain noise covariance matrices are often induced by the motion of the carrier and external disturbance. To solve the problem of initial alignment with uncertain noise covariance matrices and a large initial misalignment angle in practical environment, an improved initial alignment method based on an adaptive cubature Kalman filter (ACKF) is proposed in this paper. By virtue of the idea of the variational Bayesian (VB) method, the system state, one step predicted error covariance matrix, and measurement noise covariance matrix of initial alignment are adaptively estimated together. Simulation and vehicle experiment results demonstrate that the proposed method can improve the accuracy of initial alignment compared with existing methods.

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Section: The Proposed Adaptive Cubature Kalman Filtermentioning

confidence: 99%

An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

Zhang

Liu

2019

Sensors

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“…Furthermore, the slave and master SINS are always positioned at distance from each other, and there will be relative motion between them while the vehicle turns or manoeuvres, this will introduce additional errors into the TA. The TA method based on inertial measurement matching have received extensive attention in recent years 18,[27][28][29][30][31][32][33][34][35][36][37][38][39][40] , which can be depicted in Fig. 1.…”

Section: Transfer Alignment Of Sinsmentioning

confidence: 99%

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Dai

Tang³

et al. 2019

Def. Sc. Jl.

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Transfer alignment (TA) is an important step for strapdown inertial navigation system (SINS) starting from a moving base, which utilises the information proposed from the higher accurate and well performed master inertial navigation system. But the information is often delayed or even lost in real application, which will seriously affect the accuracy of TA. This paper models the stochastic measurement packet dropping as an independent identically distributed (IID) Bernoulli random process, and introduces it into the measurement equation of rapid TA, and the influence of measurement packet dropping is analysed. Then, it presents a suboptimal estimator for the estimation of the misalignment in TA considering the random arrival of the measurement packet. Simulation has been done for the performance comparison about the suboptimal estimator, standard Kalman filter and minimum mean squared estimator. The results show that the suboptimal estimator has better performance, which can achieve the best TA accuracy. 7. dai, h.; dai, S.; Cong, y.; Zhao, g. & wu, g. Rapid transfer alignment of laser sins using quaternion based angular measurement.

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“…Traditionally, the coarse alignment is usually accomplished analytically from gyroscope/accelerometer measurements of inertial measurement unit (IMU) [11]. Due to low signal to noise ratio of gyroscope in non-static base, however, the analytic coarse alignment is generally applicable to static alignment [12]. As a result, the attitude determination-based initial alignment (ADIA) method has been proposed for non-static coarse alignment [13,14].…”

Section: Introductionmentioning

confidence: 99%

Multistage Attitude Determination Alignment for Velocity-Aided In-Motion Strapdown Inertial Navigation System with Different Velocity Models

Gao

Liu

2019

Sensors

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A novel multistage attitude determination alignment algorithm with different velocity models is proposed to implement the alignment process of in-motion attitude determination alignment (IMADA) aided by the ground velocity expressed in body frame () in this paper. Normally, The-based IMADA is used to achieve the coarse alignment for strapdown inertial navigation system (SINS). The higher the coarse alignment accuracy, the better initial condition can be achieved to guarantee the performance of the subsequent fine alignment. Consider the influence of the principal model errors and the calculation errors on the alignment accuracy in traditional-based IMADA, this paper deals with a novel alignment algorithm by integrating two different velocity-based IMADAs and the multiple repeated alignment processes. The power of this novel alignment algorithm lies in eliminating the principal model errors and decreasing the calculation errors. Then, the higher alignment accuracy is achieved. Simulations and vehicle experiment are performed to demonstrate the validity of the proposed algorithm.

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Analytic Coarse Transfer Alignment Based on Inertial Measurement Vector Matching and Real-Time Precision Evaluation

Cited by 32 publications

References 12 publications

An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Multistage Attitude Determination Alignment for Velocity-Aided In-Motion Strapdown Inertial Navigation System with Different Velocity Models

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