A Matching Algorithm Based on the Nonlinear Filter and Similarity Transformation for Gravity-Aided Underwater Navigation

Han, Yurong; Wang, Bo; Deng, Zhihong; Fu, Mengyin

doi:10.1109/tmech.2018.2806350

Cited by 22 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, we make use of the error between the actual value and the measured value of local gravity acceleration, based on the light intensity measurement information of the polarization sensor, with the help of the accelerometer output of the inertial navigation system itself as the measurement [12]. In the process of a maneuver, the specific force that the three-axis accelerometer can be sensitive to is different according to the different maneuver paths.…”

Section: Gravity Filteringmentioning

confidence: 99%

Multi-Source Error Calibration and Observability Analysis of Integrated Inertial Navigation System/Polarization Sensor Navigation System

Zhang

Liu²,

Dou³

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Accurate polarization information acquisition is the key technique of the integrated inertial navigation system (INS)/polarization sensor (PS) navigation system. To solve the problem of poor performance of polarization sensors that real-time error calibration by using a rotary table, we propose a fast calibration method that polarization sensor combined with inertial navigation systems. Firstly, we construct the calibration model of polarization sensor. A tightly coupled INS/PS calibration model is proposed, specifically, the polarization sensor errors, which are the intensity gain coefficient, the degree of polarization coefficient, and the installation angle error of a polarizer, are augmented into the state of the integrated navigation systems. By introducing the error term of polarization sensor into the state of the integrated navigation systems, the error estimation can be carried out and the speed of error calibration can be improved. Secondly, considering the nonlinearity of error calibration model, we use the Unscented Kalman Filter (UKF) to calibrate the error. On this basis, we make use of the observability analysis to verify its validity of the proposed model in a dynamic scene. Finally, real-world experiments are conducted on the self-developed multi-channel polarization sensor. The experimental results show that the calibration method can be done quickly without the use of a rotary table.

show abstract

Section: Gravity Filteringmentioning

confidence: 99%

Multi-Source Error Calibration and Observability Analysis of Integrated Inertial Navigation System/Polarization Sensor Navigation System

Zhang

Liu²,

Dou³

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Figures 2-5 show the simulation results based on the parameters mentioned above. The above picture in Figure 2 plots the state trajectory and its estimate for x 1 k and the picture below shows the state trajectory and the estimate for x 2 k . The fault signals as well as its estimates are shown in Figure 3.…”

Section: Example 1 the System Under Consideration In (1) Has The Folmentioning

confidence: 99%

“…State estimation/filtering problems have always been one of the fundamental issues in the areas of target tracking, navigation and positioning, electric power systems, econometrics, biosystems, etc. Therefore, enormous research attention has been paid to the state estimation problems and some elegant work has been reported, see e.g., [ 1 , 2 , 3 , 4 , 5 ]. According to different performance indices, the current state estimation approaches include Kalman filtering (KF), extend Kalman filtering (EKF),

filtering and so on.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggering State and Fault Estimation for a Class of Nonlinear Systems Subject to Sensor Saturations

Huang

Shen

Zou

et al. 2021

Sensors

View full text Add to dashboard Cite

This paper is concerned with the state and fault estimation issue for nonlinear systems with sensor saturations and fault signals. For the sake of avoiding the communication burden, an event-triggering protocol is utilized to govern the transmission frequency of the measurements from the sensor to its corresponding recursive estimator. Under the event-triggering mechanism (ETM), the current transmission is released only when the relative error of measurements is bigger than a prescribed threshold. The objective of this paper is to design an event-triggering recursive state and fault estimator such that the estimation error covariances for the state and fault are both guaranteed with upper bounds and subsequently derive the gain matrices minimizing such upper bounds, relying on the solutions to a set of difference equations. Finally, two experimental examples are given to validate the effectiveness of the designed algorithm.

show abstract

“…Because the inertial navigation system (INS) is prone to accumulate errors over time, underwater vehicles need to use other physical information combined with INS for positioning navigation. The gravity-aided inertial navigation system (GAINS) can match the real-time gravity information with the pre-stored marine gravity anomaly map to obtain the accurate position of the underwater vehicle and correct the accumulated error of the INS [1][2][3][4][5][6][7]. The GAINS does not radiate energy or accept electromagnetic signals when obtaining gravity information.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Matching Area for Underwater Gravity-Aided Inertial Navigation Based on the Convolution Slop Parameter-Support Vector Machine Combined Method

Wang

Zheng

2021

Remote Sensing

View full text Add to dashboard Cite

This paper focuses on the selection of matching areas in the gravity-aided inertial navigation system. Firstly, the Sobel operator was used in convolution of the gravity anomaly map to obtain the feature map. The convolution slope parameters were constructed by combining the feature map and the gravity anomaly map. The characteristic parameters, such as the difference between convolution rows and columns, convolution variance of the feature map, the pooling difference, and range of the gravity anomaly map, were combined. Based on the support vector machine algorithm, the convolution slope parameter–support vector machine combined method is proposed. Second, we selected the appropriate training sample set and set parameters to verify. The results show that compared with the pre-calibration results, the classification accuracy of the test set is more than 92%, which proves that the convolution slope parameter–support vector machine combined method can effectively distinguish between the suitable and the unsuitable area. Thirdly, we applied this method to another region. The navigation experiment was performed in the split-matching area. The average positioning error was better than 100 m, and the correct rate was more than 90%. The results show that sailing in the selected area can accurately match the trajectory and reduce the positioning error.

show abstract

A Matching Algorithm Based on the Nonlinear Filter and Similarity Transformation for Gravity-Aided Underwater Navigation

Cited by 22 publications

References 22 publications

Multi-Source Error Calibration and Observability Analysis of Integrated Inertial Navigation System/Polarization Sensor Navigation System

Multi-Source Error Calibration and Observability Analysis of Integrated Inertial Navigation System/Polarization Sensor Navigation System

Event-Triggering State and Fault Estimation for a Class of Nonlinear Systems Subject to Sensor Saturations

Optimizing Matching Area for Underwater Gravity-Aided Inertial Navigation Based on the Convolution Slop Parameter-Support Vector Machine Combined Method

Contact Info

Product

Resources

About