Modified Sage‐Husa Adaptive Kalman Filter‐Based SINS/DVL Integrated Navigation System for AUV

Liu, Ruixin; Liu, Fucheng; Liu, Chunning; Zhang, Pengchao

doi:10.1155/2021/9992041

Cited by 19 publications

(11 citation statements)

References 15 publications

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“…First, it is determined whether the measurement noise satisfies positive definite. If not, the subtraction term is removed according to the method in literature [27], and the following measurement noise estimation relationship is used:…”

Section: Modified Adaptive Sage-husa Kalman Filtermentioning

confidence: 99%

A Modified Strong Tracking Adaptive Kalman Filter for Precision Clock Synchronization System

Zhang,

Hou

2023

IEEJ Transactions Elec Engng

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In order to reduce the clock offset caused by network delay jitter, network asymmetry, clock drift and clock frequency difference in the clock synchronization process of IEEE1588, a modified Strong Tracking Adaptive Kalman Filter (MSTAKF) algorithm was proposed. The Kalman filter model established by the algorithm is based on clock offset, network delay, frequency deviation and asymmetry ratio. The filter includes the modified strong tracking algorithm based on fuzzy logic and hypothesis test proposed in this paper, which can effectively eliminate the influence of outliers, and finally make the model tracking effect better. In addition, the modified Sage‐Husa algorithm based on Cholesky decomposition is also included, which makes the measurement noise more stable and ensures the positivity of the measurement noise covariance. The experimental results show that the proposed MSTAKF is smoother than Karman filter (KF), Sage‐Husa adaptive Kalman filter (AKF) and strong tracking Sage‐Husa adaptive Kalman filter (STAKF). Compared with these filter algorithms after testing in the LAN environment, the precision improvement and accuracy rate are significantly improved. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Section: Modified Adaptive Sage-husa Kalman Filtermentioning

confidence: 99%

A Modified Strong Tracking Adaptive Kalman Filter for Precision Clock Synchronization System

Zhang,

Hou

2023

IEEJ Transactions Elec Engng

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“…Although the posterior estimator in Sage-Husa adaptive filtering can effectively reduce data error [10], it has been widely applied in many fields. However, there are still some defects and shortcomings.…”

Section: Modificative Sage-husa Adaptive Kalman Filtermentioning

confidence: 99%

Filtering processing of belt scale signal based on MATLAB

Zhang

Hao³

et al. 2022

International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022)

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The weighing accuracy is an important index of electronic belt scale. It is difficult to achieve accurate measurement due to the influence of environmental temperature and humidity, belt tension and mechanical vibration. The filtering algorithm is introduced into the belt scale data acquisition and processing system. By filtering the collected data can eliminate or reduce the system error and improve the weighing accuracy. By studying the composition structure, weighing principle and error source of belt scale weighing system to analyze and test three filtering algorithms. By comparing the experimental data, an improved adaptive filter algorithm can play a better role and improve the weighing accuracy. It is important to improve the dynamic weighing accuracy of electronic belt scale and reduce the error.

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“…represents the mainline of the integrated positioning model, which realizes SIN, real-time pose shifting, 3-axis position output, speed and attitude. The purple axis represents the correction solution, which realizes Sage-Husa adaptive filtering and NLOS discrimination etc., and gives the error amount of the current state of the system [49]. The red axis represents the filter feedback, and the error amount of the measurement update solution fed back to the system state.…”

Section: Imu Sensor Positioning Processing Algorithmmentioning

confidence: 99%

“…The innovation sequence of the filter is equal to the difference between a priori measurement estimate and the posterior sensor measurement. To a certain extent, the innovation value reflects the performance of AKF estimation, so Sage-Husa adaptive estimation uses this feature to identify process and measurement noises [49]. Let r be the measured noise's deviation value; then the noises characteristic identifies as (12).…”

Section: Adaptive Kalman Filter Estimation In Nlosmentioning

confidence: 99%

Tightly Coupling Fusion of UWB Ranging and IMU Pedestrian Dead Reckoning for Indoor Localization

et al. 2021

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Ultra-wideband (UWB) and inertial measurement unit (IMU) fusion is an efficient method to resolve the uncertainties of UWB in non-line-of-sight (NLOS) situations because of signals refraction, the effect of multipath and inertial positioning error accumulation in indoor environments. Existing systems, however, are focused only on foot-mounted IMUs that restrict the system's implementation to particular real situations. In this research, using foot-mounted IMU, we suggest combining UWB ranging and IMU pedestrian dead reckoning (PDR), which can provide a generic indoor positioning solution. The issues such as position and orientation drift, interferences and divergence in strap-down inertial navigation system (SINS) based orientation estimates could be addressed by a UWB ranging sensor fusing with an IMU using the extended Kalman filter (EKF). The main goal of this research is to investigate and compare two different sensor data fusion techniques. For instance, adaptive Kalman filter (AKF) and least-squares (LSs) incorporate a foot-mounted IMU tightly coupled to a 2D pedestrian positioning solution derived from UWB signals. Moreover, we consider the UWB NLOS and IMU error identification. A real-time ranging error compensation model based on the LS method and AKF positioning algorithm are used for fixing such problems. We propose a new tightly coupled inertial navigation system (INS) with a two-way ranging (TWR) fusion positioning algorithm to improve accuracy, integrating UWB and IMU sensors based on the EKF in pedestrian navigation. Experiments in dynamic indoor environment validate the effectiveness of the proposed approach that uses EKF to combine AKF and LS for error minimization.

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Modified Sage‐Husa Adaptive Kalman Filter‐Based SINS/DVL Integrated Navigation System for AUV

Cited by 19 publications

References 15 publications

A Modified Strong Tracking Adaptive Kalman Filter for Precision Clock Synchronization System

A Modified Strong Tracking Adaptive Kalman Filter for Precision Clock Synchronization System

Filtering processing of belt scale signal based on MATLAB

Tightly Coupling Fusion of UWB Ranging and IMU Pedestrian Dead Reckoning for Indoor Localization

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