Kalman Filter for Cross-Noise in the Integration of SINS and DVL

The integration of strapdown inertial navigation system and Doppler velocity log (SINS/DVL) is widely used for navigation in automatic underwater vehicles (AUVs). In the integration of SINS/DVL, the velocity measured by DVL in body frame should be projected into navigation frame with the help of attitude matrix calculated by SINS to participate in data fusion. In the process of data fusion based on standard Kalman filter, the errors in calculated attitude matrix are characterized by state variance and process noise while the errors in measurement vector from DVL are by measurement noise. But the above projection will bring process noise into measurement noise, and thus the assumption of the independence between process noise and measurement noise will not stand. In this paper, the forming mechanism of cross-noise in SINS/DVL is studied in detail and Kalman filter for cross-noise is introduced to deal with this problem. Simulation results indicate that navigation accuracy, especially the position accuracy, can be improved when the cross-noise is processed in Kalman filter.

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Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

Journal of Navigation ◽

10.1017/s0373463320000570 ◽

2020 ◽

pp. 1-21

Author(s):

Lanhua Hou ◽

Xiaosu Xu ◽

Yiqing Yao ◽

Di Wang ◽

Jinwu Tong

Keyword(s):

Kalman Filter ◽

Inertial Navigation System ◽

Moving Average ◽

Integrated System ◽

Measurement Noise ◽

Noise Estimation ◽

Doppler Velocity ◽

Forgetting Factor ◽

Exponential Weighted Moving Average ◽

Weighted Moving Average

Abstract The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

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Discrete EKF with pairwise Time Correlated Measurement Noise for Image-Aided Inertial Integrated Navigation

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-2-61-2014 ◽

2014 ◽

Vol II-2 ◽

pp. 61-66 ◽

Cited By ~ 2

Author(s):

N. S. Gopaul ◽

J. G. Wang ◽

B. Hu

Keyword(s):

Kalman Filter ◽

Inertial Navigation ◽

Measurement Noise ◽

Integrated Navigation ◽

Process Noise ◽

Shaping Filter ◽

Egomotion Estimation ◽

Simulation Results ◽

The One ◽

Better Than

An image-aided inertial navigation implies that the errors of an inertial navigator are estimated via the Kalman filter using the aiding measurements derived from images. The standard Kalman filter runs under the assumption that the process noise vector and measurement noise vector are white, i.e. independent and normally distributed with zero means. However, this does not hold in the image-aided inertial navigation. In the image-aided inertial integrated navigation, the relative positions from optic-flow egomotion estimation or visual odometry are pairwise correlated in terms of time. It is well-known that the solution of the standard Kalman filter becomes suboptimal if the measurements are colored or time-correlated. Usually, a shaping filter is used to model timecorrelated errors. However, the commonly used shaping filter assume that the measurement noise vector at epoch k is not only correlated with the one from epoch k – 1 but also with the ones before epoch k – 1 . The shaping filter presented in this paper uses Cholesky factors under the assumption that the measurement noise vector is pairwise time-correlated i.e. the measurement noise are only correlated with the ones from previous epoch. Simulation results show that the new algorithm performs better than the existing algorithms and is optimal.

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The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

International Journal of Adaptive Control and Signal Processing ◽

10.1002/acs.3148 ◽

2020 ◽

Vol 34 (10) ◽

pp. 1321-1340

Author(s):

Xuehai Wang ◽

Fang Zhu ◽

Feng Ding

Keyword(s):

Kalman Filter ◽

Nonlinear Systems ◽

Extended Kalman Filter ◽

Recursive Estimation ◽

Measurement Noise ◽

Process Noise

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Weighted Multi-Sensor Data Fusion Based on Fuzzy Kalman Filter for Seam Tracking of the Welding Robots

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.542-543.800 ◽

2012 ◽

Vol 542-543 ◽

pp. 800-805 ◽

Cited By ~ 1

Author(s):

Jun Du ◽

Mei Sun ◽

Liang Hua ◽

Jia Sheng Ge ◽

Ju Ping Gu

Keyword(s):

Kalman Filter ◽

Data Fusion ◽

Maximum Modulus ◽

Seam Tracking ◽

Measurement Noise ◽

Sensor Data ◽

Inference System ◽

Sensor Data Fusion ◽

Noise Characteristics ◽

Multi Sensor Data Fusion

In order to resolve the problem of seam tracking of the welding robots with unknown noise characteristics, a Weighted Multi-Sensor Data Fusion (MSDF) algorithm based on the fuzzy Kalman filter algorithm is proposed. Firstly, each Fuzzy Kalman Filter (FKF) uses a fuzzy inference system based on a covariance matching technique to adjust the weight coefficient of measurement noise covariance matrix, so it makes measurement noise close to the true noise level. Secondly, a membership function in fuzzy set is used to measure the mutual support degree matrix of each FKF and corresponding weight coefficients are allocated by this matrix’s maximum modulus eigenvectors, hence, the final expression of data fusion is obtained. Finally, simulation results show that MSDF in seam tracking has both high precision and strong ability of stableness.

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The Selection of Process Noise Covariance Q and Measurement Noise Covariance R on Kalman Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.4342 ◽

2014 ◽

Vol 513-517 ◽

pp. 4342-4345 ◽

Cited By ~ 2

Author(s):

Chao Yi Wei ◽

Shu Jian Ye ◽

Xu Guang Li ◽

Mei Zhi Xie ◽

Feng Yan Yi

Keyword(s):

Kalman Filter ◽

Simulation Model ◽

Dynamic Model ◽

Measurement Noise ◽

State Estimator ◽

Process Noise ◽

Measurement Noise Covariance ◽

High Measurement ◽

Noise Covariance ◽

Selection Of

At first, a seven degree of freedom dynamic model of tractor semi-trailer was established, and a simulation model with the Matlab/Simulink software was established too. Based on the model, a state estimator based on Kalman theory was designed, using easily measured parameters to estimate the parameters that are difficultly measured or have high measurement cost, after that, compared estimated values and simulation measurements and analyzed the influence with changes of process noise covariance Q and measurement noise covariance R.

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Huber’s M-Estimation-Based Cubature Kalman Filter for an INS/DVL Integrated System

Mathematical Problems in Engineering ◽

10.1155/2020/1060672 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Yao Li ◽

Lanhua Hou ◽

Yang Yang ◽

Jinwu Tong

Keyword(s):

Kalman Filter ◽

State Equation ◽

Integrated System ◽

Doppler Velocity ◽

Loosely Coupled ◽

Underwater Environment ◽

Cubature Kalman Filter ◽

M Estimation ◽

Vehicle Test ◽

Navigation Accuracy

To deal with the problems of outliers and nonlinearity in the complex underwater environment, a Huber’s M-estimation-based cubature Kalman filter (CKF) is proposed for an inertial navigation system (INS)/Doppler velocity log (DVL) integrated system. First, a loosely coupled INS/DVL integrated system is designed, and the nonlinear system model is established in the case of big misalignment angle. Then, Huber’s M-estimation is introduced for robust estimation to resist outliers. Meanwhile, the CKF is focused to handle the nonlinearity of the state equation. Finally, simulation and the vehicle test are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the conventional Kalman filter (KF) and outlier detection-based robust cubature Kalman filter (RCKF) in terms of navigation accuracy in the complex underwater environment.

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Data Assimilation of River Networks Using Extended Kalman Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.580-583.1923 ◽

2014 ◽

Vol 580-583 ◽

pp. 1923-1927

Author(s):

Yi Fan Chen ◽

Jing Lin Qian

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Real Time ◽

Extended Kalman Filter ◽

River Network ◽

Measurement Noise ◽

River Networks ◽

Network System ◽

Process Noise ◽

Measurement Noises

In order to improve the accuracy of river network hydraulic model, extended kalman filter was used for real-time updating model states. In a simulation example of a river network composed of 14 channels, it systematically analyzed the effects of process and measurement noises on state correction. The results show that the extended kalman filter is able to effectively carry out data assimilation of non-linear river network system, and big process noise in combination with relatively small measurement noise is recommended for state correction.

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