Initial Alignment for a Doppler Velocity Log-Aided Strapdown Inertial Navigation System With Limited Information

Chang, Lubin; Li, Yang; Xue, Binghan

doi:10.1109/tmech.2016.2616412

Cited by 109 publications

(38 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Kalman filter (KF) is a powerful and most common technique to estimate the unknown state of the system, and it has been widely used for the initial alignment of the SINS [18,19]. The performance of traditional KF is heavily depends on precise prior knowledge of the process noise covariance matrix (Q) and the measurement noise covariance matrix (R).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The Strap-down Inertial Navigation System (SINS) solves the motion parameters, including the velocity and the position of vehicle, relative to known positions by integral operation [Titterton, Weston and Weston (2004); Chang, Li and Xue (2017)]. Because of the sensor error, the position error of SINS will accumulate slowly with the integration operation [Chang, Li and Xue (2017); Huang, Zhang and Wang (2017)]. Therefore, the initial motion parameters of the carrier are critical to the SINS.…”

Section: Introductionmentioning

confidence: 99%

A Coarse Alignment Based on the Sliding Fixed-Interval Least Squares Denoising Method

Zhu¹,

Zhang²,

Li³

et al. 2019

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The observation vectors in traditional coarse alignment contain random noise caused by the errors of inertial instruments, which will slow down the convergence rate. To solve the above problem, a real-time noise reduction method, sliding fixed-interval least squares (SFI-LS), is devised to depress the noise in the observation vectors. In this paper, the least square method, improved by a sliding fixed-interval approach, is applied for the real-time noise reduction. In order to achieve a better-performed coarse alignment, the proposed method is utilized to de-noise the random noise in observation vectors. First, the principles of proposed SFI-LS algorithm and coarse alignment are devised. A simulation test and turntable experiment were executed to demonstrate the availability of the designed method. It is indicated that, from the results of the simulation and turntable tests, the designed algorithm can effectively reduce the random noise in observation vectors. Therefore, the proposed method can enhance the performance of coarse alignment availably.

show abstract

“…The OBA algorithm obtains optimal attitude matrix through the q method to reduce random errors of attitude angles, however, these algorithms in [15,17] are not suitable for odometer-aided SINS, since the odometer can only provide the velocity in body frame. To solve this problem, the OBA algorithm with the aid of external velocity provided by odometer is reported in [19,20,21]. However, the outputs of inertial sensors and odometer in one sampling interval are assumed to be a constant, which may result in errors of coarse alignment when the velocity and acceleration change.…”

Section: Introductionmentioning

confidence: 99%

An Improved Coarse Alignment Algorithm for Odometer-Aided SINS Based on the Optimization Design Method

Zhang

Luo

Fang

et al. 2018

Sensors

View full text Add to dashboard Cite

An improved coarse alignment (ICA) algorithm is proposed in this paper with a focus on improving alignment accuracy of odometer-aided strapdown inertial navigation system (SINS) under variable velocity and variable acceleration condition. In the proposed algorithm, the outputs of inertial sensors and odometer in a sampling interval are linearized rather than assumed to be a constant, which improves the accuracy of the vector observations and the precision of coarse alignment. Simulation and field test results illustrate that, under variable velocity and variable acceleration condition, the proposed algorithm can obtain a better alignment performance than conventional coarse alignment method.

show abstract

Initial Alignment for a Doppler Velocity Log-Aided Strapdown Inertial Navigation System With Limited Information

Cited by 109 publications

References 30 publications

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

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

A Coarse Alignment Based on the Sliding Fixed-Interval Least Squares Denoising Method

An Improved Coarse Alignment Algorithm for Odometer-Aided SINS Based on the Optimization Design Method

Contact Info

Product

Resources

About