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

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.

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

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.

scholarly journals Mars Cruise Orbit Determination from Combined Optical Celestial Techniques and X-ray Pulsars

2017 ◽  
Vol 70 (4) ◽  
pp. 719-734 ◽  
Author(s):  
Jiandong Liu ◽  
Erhu Wei ◽  
Shuanggen Jin
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Integrated System ◽  
Observation Data ◽  
X Ray ◽  
Celestial Navigation ◽  
Federated Kalman Filter ◽  
Mars Pathfinder ◽  
Navigation Accuracy

The precise autonomous navigation for deep space exploration by combination of multi-source observation data is a key issue for probe control and scientific applications. In this paper, the performance of an integrated Optical Celestial Navigation (OCN) and X-ray Pulsars Autonomous Navigation (XNAV) system is investigated for the orbit of Mars Pathfinder. Firstly, OCN and XNAV single systems are realised by an Unscented Kalman Filter (UKF). Secondly, the integrated system is simulated with a Federated Kalman Filter (FKF), which can do the information fusion of the two subsystems of UKF and inherits the advantages of each subsystem. Thirdly, the performance of our system is evaluated by analysing the relationship between observation errors and navigation accuracy. The results of the simulation experiments show that the biases between the nominal and our calculated orbit are within 5 km in all three axes under complex error conditions. This accuracy is also better than current ground-based techniques.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Xixiang Liu ◽  
Xiaosu Xu ◽  
Yiting Liu ◽  
Lihui Wang
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Measurement Noise ◽  
Doppler Velocity ◽  
Body Frame ◽  
Process Noise ◽  
Forming Mechanism ◽  
Measurement Vector ◽  
Navigation Accuracy ◽  
Attitude Matrix

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.

scholarly journals A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1717 ◽  
Author(s):  
Jing Hou ◽  
He He ◽  
Yan Yang ◽  
Tian Gao ◽  
Yifan Zhang
Keyword(s):  
Kalman Filter ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Constant Current ◽  
Square Root ◽  
Variational Bayesian ◽  
Soc Estimation ◽  
Cubature Kalman Filter ◽  
M Estimation

An accurate state of charge (SOC) estimation is vital for safe operation and efficient management of lithium-ion batteries. To improve the accuracy and robustness, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation (VB-HASRCKF) is proposed. The variational Bayesian (VB) approximation is used to improve the adaptivity by simultaneously estimating the measurement noise covariance and the SOC, while Huber’s M-estimation is employed to enhance the robustness with respect to the outliers in current and voltage measurements caused by adverse operating conditions. A constant-current discharge test and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the square root cubature Kalman filter (SRCKF), the VB-based SRCKF, and the Huber-based SRCKF. The experimental results show that the proposed VB-HASRCKF algorithm outperforms the other three filters in terms of SOC estimation accuracy and robustness, with a little higher computation complexity.

scholarly journals Robust Huber-Based Cubature Kalman Filter for GPS Navigation Processing

2016 ◽  
Vol 70 (3) ◽  
pp. 527-546 ◽  
Author(s):  
Chien-Hao Tseng ◽  
Sheng-Fuu Lin ◽  
Dah-Jing Jwo
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Unscented Kalman Filter ◽  
Measurement Information ◽  
Gps Navigation ◽  
Cubature Kalman Filter ◽  
Global Positioning ◽  
M Estimation ◽  
Non Gaussian ◽  
Distribution Assumption

A robust state estimation technique based on the Huber-based Cubature Kalman Filter (HCKF) is proposed for Global Positioning System (GPS) navigation processing. The Cubature Kalman Filter (CKF) employs a third-degree spherical-radial cubature rule to compute the Gaussian weighted integration, such that the numerical instability induced by round-off errors can be avoided. In GPS navigation, the filter-based estimation of the position and velocity states can be severely degraded due to contaminated measurements caused by outliers or deviation from a Gaussian distribution assumption. For the signals contaminated with non-Gaussian noise or outliers, a robust scheme combining the Huber M-estimation methodology and the CKF framework is beneficial where the Huber M-estimation methodology is used to reformulate the measurement information of the CKF. GPS navigation processing using the HCKF algorithm has been carried out and the performance has been compared to those based on the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and CKF approaches. Simulation and experimental results presented in this paper confirm the effectiveness of the method.

Ultra-tight GPS/IMU Integration based Long-Range Rocket Projectile Navigation

2016 ◽  
Vol 66 (1) ◽  
pp. 64 ◽  
Author(s):  
Handong Zhao ◽  
Zhipeng Li
Keyword(s):  
Kalman Filter ◽  
Long Range ◽  
High Performance ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Position Information ◽  
Cubature Kalman Filter ◽  
Carrier Phase Tracking ◽  
Output Information ◽  
Navigation Accuracy

<p>Accurate navigation is important for long-range rocket projectile’s precise striking. For getting a stable and high-performance navigation result, a ultra-tight global position system (GPS), inertial measuring unit integration (IMU)-based navigation approach is proposed. In this study, high-accuracy position information output from IMU in a short time to assist the carrier phase tracking in the GPS receiver, and then fused the output information of IMU and GPS based on federated filter. Meanwhile, introduced the cubature kalman filter as the local filter to replace the unscented kalman filter, and improved it with strong tracking principle, then, improved the federated filter with vector sharing theory. Lastly simulation was carried out based on the real ballistic data, from the estimation error statistic figure. The navigation accuracy of the proposed method is higher than traditional method.</p><p><strong>Defence Science Journal, Vol. 66, No. 1, January 2016, pp. 64-70, DOI: http://dx.doi.org/10.14429/dsj.66.8326</strong></p>

scholarly journals A Robust Adaptive Cubature Kalman Filter Based on SVD for Dual-Antenna GNSS/MIMU Tightly Coupled Integration

2021 ◽  
Vol 13 (10) ◽  
pp. 1943
Author(s):  
Cheng Pan ◽  
Nijia Qian ◽  
Zengke Li ◽  
Jingxiang Gao ◽  
Zhenbin Liu ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Satellite System ◽  
Integrated System ◽  
Measurement Unit ◽  
Attitude Angle ◽  
Cubature Kalman Filter ◽  
Tightly Coupled ◽  
Heading Angle ◽  
Robust Adaptive

In complex urban environments, a single Global Navigation Satellite System (GNSS) is often not ideal for navigation due to a lack of sufficient visible satellites. Additionally, the heading angle error of a GNSS/micro-electro-mechanical system–grade inertial measurement unit (MIMU) tightly coupled integration based on the single antenna is large, and the attitude angle, velocity, and position calculated therein all have large errors. Considering the above problems, this paper designs a tightly coupled integration of GNSS/MIMU based on two GNSS antennas and proposes a singular value decomposition (SVD)-based robust adaptive cubature Kalman filter (SVD-RACKF) according to the model characteristics of the integration. In this integration, the high-accuracy heading angle of the carrier is obtained through two antennas, and the existing attitude angle information is used as the observation to constrain the filtering estimation. The proposed SVD-RACKF uses SVD to stabilize the numerical accuracy of the recursive filtering. Furthermore, the three-stage equivalent weight function and the adaptive adjustment factor are constructed to suppress the influence of the gross error and the abnormal state on the parameter estimation, respectively. A set of real measured data was employed for testing and analysis. The results show that dual antennas and dual systems can improve the positioning performance of the integrated system to a certain extent, and the proposed SVD-RACKF can accurately detect the gross errors of the observations and effectively suppress them. Compared with the cubature Kalman filter, the proposed filtering algorithm is more robust, with higher accuracy and reliability of parameter estimation.

Sign in / Sign up

Export Citation Format

Share Document