Author
Lijuan Li
Bio: Lijuan Li is an academic researcher from Harbin Engineering University. The author has contributed to research in topics: Inertial navigation system & Algorithm design. The author has an hindex of 2, co-authored 2 publications receiving 19 citations.
Papers
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26 Aug 2004
TL;DR: This paper analyzes and compares the attitude error and velocity error of the above algorithm with other algorithms' and shows that the synchro-updating algorithm is more effective than the others for the angular rate outputs and the specific force outputs.
Abstract: This paper proposes an approach to update the attitude and velocity synchronously utilizing the fourth order Runge-Kutta in the strapdown inertial navigation system. The synchro-updating algorithm is designed for the condition, in which the outputs of gyroscope are the angular rate and the outputs of accelerometer are the specific force acceleration. The synchro-updating algorithm is based on the quaternion. Under the improved classical coning motion and the generalized vibrational motion respectively, this paper analyzes and compares the attitude error and velocity error of the above algorithm with other algorithms'. The simulation results show that the synchro-updating algorithm is more effective than the others for the angular rate outputs and the specific force outputs.
10 citations
29 Jul 2005
TL;DR: The result of simulation and test shows perfect knowledge of the a prior information will be only of secondary importance when the estimator selects the FAKF to achieve integrated navigation, not conventional Kalman filter (CKF).
Abstract: The integrated INS/GPS navigation system, which is applied to the marine, is necessary to provide long-term high accurate navigation information. A fuzzy adaptive Kalman filter (FAKF) is developed to estimate the navigational information accurately, and achieve the in-flight alignment and positioning. The proposed algorithm adaptively changes the corresponding weighted factor via fuzzy logic for every observable, and utilizes the weighted matrixes to adjust the Kalman filter. The weighted-matrixes come from four channels, which respectively respond to the residuals of latitude, longitude, east velocity and north velocity, in the fuzzy logic controller. The result of simulation and test shows perfect knowledge of the a prior information will be only of secondary importance when the estimator selects the FAKF to achieve integrated navigation, not conventional Kalman filter (CKF). In the case of insufficiently known a prior statistics, the in-flight alignment and positioning performance of FAKF is better than CKF, and FAKF is more efficient.
10 citations
Cited by
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TL;DR: This paper is among the first surveys which provide such breadth of coverage across different SFAs for tracking orientation with MIMUs, and has identified the need for benchmarking studies as the main challenge at the moment.
62 citations
TL;DR: In this article, a novel method processing the gyro and accelerometer measurements with infinite impulse response (IIR) digital low-pass filter to remove the high frequency noise is investigated for marine mooring alignment.
43 citations
TL;DR: The proposed FTKF can complete data fusion for multi-height sensors, detect faulty sensors online and conduct fault tolerance in real time, and may achieve the expected fault-tolerant performance in integrated navigation system of UAV.
30 citations
23 May 2009
TL;DR: In this paper, an initial misalignment angle transfer alignment model of AINS was established, which is other than traditional velocity and attitude matching algorithm. But the model is specific to rigidity carrier ship, and it deduces the model equations of Airborne Inertial Navigation System's velocity and acceleration with the effect of arm-lever vector.
Abstract: Proceeding from actual needs of AINS initial alignment technology, this paper is specific to rigidity carrier ship, and it deduces the model equations of Airborne Inertial Navigation System's velocity and acceleration (ecificforce) with the effect of arm-lever vector. It employs the method that establishes large initial misalignment angle transfer alignment model of AINS, which is other than traditional velocity and attitude matching algorithm. Basing on nonlinear deterministic particle filtering, it simulates the performance of the model with experimental data, and comparatively studies inhering lever-arm vector error's influence on AINS misalignment attitude angle estimation error and its alignment accuracy. With different initial lever-arm vectors it researches its effects on misalignment attitude angle's estimation error and the accuracy of transfer alignment. The simulation results verify the model's validity and its superior alignment accuracy in transfer alignment of AINS. It is of great theory and application value in design of AINS.
12 citations
05 Jan 2009
TL;DR: A newly developed Fuzzy Adaptive Kalman Filter (FAKF) algorithm is presented which is applied in miniature Attitude and Heading Reference System (AHRS) based on MIMU/magnetometers to deal with time variable statistic of measurement noise in different working conditions.
Abstract: In the paper a newly developed Fuzzy Adaptive Kalman Filter (FAKF) algorithm is presented which is applied in miniature Attitude and Heading Reference System (AHRS) based on MIMU/magnetometers. The method is to deal with time variable statistic of measurement noise in different working conditions. By monitoring the innovation of sensors data in realtime, the Kalman filter tunes the measurement noise covariance matrix and process noise covariance matrix on-line according to fuzzy logic inference system to get the optimal state estimation. The test results indicate that the algorithm of FAKF has better accuracy than the regular Kalman Filter.
12 citations