Characteristics of Marine Gravity Anomaly Reference Maps and Accuracy Analysis of Gravity Matching-Aided Navigation

Wang, Hubiao; Wu, Lin; Chai, Hua; Xiao, Yaofei; Hsu, Houtse; Wang, Yong

doi:10.3390/s17081851

Cited by 34 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scalar field based localization system designs have been researched in applications such as gravity-aid navigation [ 11 , 18 , 19 ], magnetic anomaly–based navigation [ 7 , 20 ], and terrain-based navigation [ 21 ]. In order to perform robust localization in featureless areas or with low-quality sensors, cooperative multi-agent localization systems are proposed to achieve accurate estimations [ 22 , 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

A Scalable Framework for Map Matching Based Cooperative Localization

Yang

Strader

2021

Sensors

View full text Add to dashboard Cite

Localization based on scalar field map matching (e.g., using gravity anomaly, magnetic anomaly, topographics, or olfaction maps) is a potential solution for navigating in Global Navigation Satellite System (GNSS)-denied environments. In this paper, a scalable framework is presented for cooperatively localizing a group of agents based on map matching given a prior map modeling the scalar field. In order to satisfy the communication constraints, each agent in the group is assigned to different subgroups. A locally centralized cooperative localization method is performed in each subgroup to estimate the poses and covariances of all agents inside the subgroup. Each agent in the group, at the same time, could belong to multiple subgroups, which means multiple pose and covariance estimates from different subgroups exist for each agent. The improved pose estimate for each agent at each time step is then solved through an information fusion algorithm. The proposed algorithm is evaluated with two different types of scalar field based simulations. The simulation results show that the proposed algorithm is able to deal with large group sizes (e.g., 128 agents), achieve 10-m level localization performance with 180 km traveling distance, while under restrictive communication constraints.

show abstract

Section: Related Workmentioning

confidence: 99%

A Scalable Framework for Map Matching Based Cooperative Localization

Yang

Strader

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The traditional statistical characteristic parameters include standard deviation, the latitude and longitude correlation, variation coefficient, kurtosis coefficient, skew coefficient etc. The traditional algorithms select the matching area by comparing and analysing the traditional statistical characteristic parameters [18][19][20]. However, the shortcomings of the traditional algorithms are uncertainty of parameter threshold and ill consideration in spatial characteristics of gravity field.…”

Section: Introductionmentioning

confidence: 99%

A co‐occurrence matrix‐based matching area selection algorithm for underwater gravity‐aided inertial navigation

Wang

Deng

et al. 2021

IET Radar Sonar & Navi

View full text Add to dashboard Cite

The matching area selection algorithm is one of the key technologies for underwater gravity‐aided inertial navigation system, which directly affects the positioning accuracy and matching rate of underwater navigation. The traditional matching area selection algorithms usually use the statistical characteristic parameters of gravity field. However, the traditional algorithms are difficult to reflect the spatial relation characteristic of gravity field, which always miss some latent matching areas with obvious change of gravity field. In order to solve this problem, the matching area selection algorithm based on co‐occurrence matrix is proposed. The proposed algorithm establishes gravity anomaly co‐occurrence matrix and extracts spatial relation characteristic parameters to reflect the gravity field. The comprehensive spatial characteristic parameter is built by entropy and is used to select the matching area by maximization of inter‐class variance. The experimental results show that the proposed algorithm can select more effective matching areas than the traditional algorithms.

show abstract

“…In 2017, Hubiao Wang and others analyzed the traditional parameters of the gravity anomaly map, constructed a type of gravity anomaly characteristic parameters, analyzed the influence of gravity changes on the accuracy of GAINS, and determined the positioning accuracy of gravity matching in different regions. Experimental results show that the parameters can better characterize ocean gravity anomaly [10]. In 2019, Fanming Liu and others used the projection pursuit method for suitability analysis, used the image texture feature analysis method to extract the navigability features of the local gravity map, and used the projection pursuit model to comprehensively evaluate the navigability of the local gravity map [11].…”

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

Characteristics of Marine Gravity Anomaly Reference Maps and Accuracy Analysis of Gravity Matching-Aided Navigation

Cited by 34 publications

References 25 publications

A Scalable Framework for Map Matching Based Cooperative Localization

A Scalable Framework for Map Matching Based Cooperative Localization

A co‐occurrence matrix‐based matching area selection algorithm for underwater gravity‐aided inertial navigation

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