An improved GNSS/INS navigation method based on cubature Kalman filter for occluded environment

Liu, Wei; Shi, Yihang; Liu, Wei; Hsieh, Tsung-Hsuan; Wang, Shengzheng

doi:10.1088/1361-6501/aca421

Cited by 10 publications

(8 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In comparison to the Unscented Kalman filter algorithm, the Cubature Kalman Filter algorithm obtains the volume points by calculating the spheri-cal radial volume criterion without linearizing the state equation directly transferring the volume points by the nonlinear state equation while ensuring that the weights are always positive. This improves the algorithm's robustness and accuracy [22][23][24].…”

Section: Target Localization Methods Based On Cubature Kalman Filtermentioning

confidence: 99%

A Cooperative Target Localization Method Based on UAV Aerial Images

Du,

Zou,

Wang

et al. 2023

Aerospace

View full text Add to dashboard Cite

A passive localization algorithm based on UAV aerial images and Angle of Arrival (AOA) is proposed to solve the target passive localization problem. In this paper, the images are captured using fixed-focus shooting. A target localization factor is defined to eliminate the effect of focal length and simplify calculations. To synchronize the positions of multiple UAVs, a dynamic navigation coordinate system is defined with the leader at its center. The target positioning factor is calculated based on image information and azimuth elements within the UAV photoelectric reconnaissance device. The covariance equation is used to derive AOA, which is then used to obtain the target coordinate value by solving the joint UAV swarm positional information. The accuracy of the positioning algorithm is verified by actual aerial images. Based on this, an error model is established, the calculation method of the co-localization PDOP is given, and the correctness of the error model is verified through the simulation of the Monte Carlo statistical method. At the end of the article, the trackless Kalman filter algorithm is designed to improve positioning accuracy, and the simulation analysis is performed on the stationary and moving states of the target. The experimental results show that the algorithm can significantly improve the target positioning accuracy and ensure stable tracking of the target.

show abstract

Section: Target Localization Methods Based On Cubature Kalman Filtermentioning

confidence: 99%

A Cooperative Target Localization Method Based on UAV Aerial Images

Du,

Zou,

Wang

et al. 2023

Aerospace

View full text Add to dashboard Cite

show abstract

“…This approach contributes to enhancing the precision of the entire navigation system. When passing through a tunnel, although the

signal is blocked and the

information is inaccurate, the attitude information in the TMR digital compass is accurate and can continue to provide attitude correction for the

[ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ].…”

Section: Design Of High-precision Portable Digital Compass Systemmentioning

confidence: 99%

Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance

Zhang,

Cui,

Zhang

2024

Sensors

View full text Add to dashboard Cite

There are not many high-precision, portable digital compass solutions available right now that can enhance combined navigation systems’ overall functionality. Additionally, there is a dearth of writing about these products. This is why a tunnel magnetoresistance (TMR) sensor-based high-precision portable digital compass system is designed. First, the least-squares method is used to compensate for compass inaccuracy once the ellipsoid fitting method has corrected manufacturing and installation errors in the digital compass system. Second, the digital compass’s direction angle data is utilized to offset the combined navigation system’s mistake. The final objective is to create a high-performing portable TMR digital compass system that will enhance the accuracy and stability of the combined navigation system (abbreviated as CNS). According to the experimental results, the digital compass’s azimuth accuracy was 4.1824° before error compensation and 0.4580° after it was applied. The combined navigation system’s path is now more accurate overall and is closer to the reference route than it was before the digital compass was added. Furthermore, compared to the combined navigation route without the digital compass, the combined navigation route with the digital compass included is more stable while traveling through the tunnel. It is evident that the digital compass system’s design can raise the integrated navigation system’s accuracy and stability. The integrated navigation system’s overall performance may be somewhat enhanced by this approach.

show abstract

“…This paper defines the inertial coordinate frame as the I-frame, the carrier coordinate frame as the b-frame, and the navigation coordinate frame as the n-frame, which has east, north, and up directions and follows the right-hand rule [24]. The error equation of attitude change with time can be obtained as shown in equation (5).…”

Section: Error Analysis Of Inertial Navigation Systemsmentioning

confidence: 99%

“…The first idea is to estimate and compensate for the modeling error and utilize various methods to approximate the real model of the system error to improve the accuracy of the integrated navigation. Examples of such approaches include adding external sensors [3], using motion constraint algorithms [4], and improving Kalman filtering algorithms [5]. The second approach involves using artificial intelligence to establish the relationship between the vehicle dynamics and the INS error for the part of the system that is nonlinear or difficult to model, and to predict the future vehicle trajectory.…”

Section: Introductionmentioning

confidence: 99%

An integrated navigation algorithm assisted by CNN-Informer during short-time GNSS outages

Hu,

Fan,

Liu

et al. 2024

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Inertial Navigation System (INS) and Global Navigation Satellite System (GNSSs) are commonly used in integrated navigation systems. The GNSS navigation system, however, is not autonomous, and its signals are susceptible to environmental influences leading to poor navigation results. In order to improve the positioning accuracy of the INS/GNSS integrated navigation system during GNSS outages, a hybrid algorithm of Convolutional Neural Network (CNN)-Informer model-assisted INS is proposed. The CNN-Informer model combines the advantages of each of the CNN and the Informer network. It utilizes CNN to rapidly extract spatial features from input data, employs the Informer network to establish relationships between inputs and outputs, and acquires pseudo-GNSS signals to compensate for position errors in the INS. Lastly, we evaluate the proposed algorithm through six sets of experiments conducted on public datasets and real road scenarios. The comparative results demonstrate that the proposed method offers a more accurate and reliable solution during GNSS outages. Specifically, CNN-Informer can improve navigation accuracy by 81.46% over MLP and by 58.91% over LSTM during 20-s GNSS outages.

show abstract

An improved GNSS/INS navigation method based on cubature Kalman filter for occluded environment

Cited by 10 publications

References 31 publications

A Cooperative Target Localization Method Based on UAV Aerial Images

A Cooperative Target Localization Method Based on UAV Aerial Images

Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance

An integrated navigation algorithm assisted by CNN-Informer during short-time GNSS outages

Contact Info

Product

Resources

About