Vector Data Fusion Research Articles

Distributed fault detection and isolation in time‐varying formation tracking UAV multi‐agent systems

AbstractThe problem of distributed fault detection and isolation (DFDI) in conjunction with time‐varying formation control of unmanned aerial vehicle (UAV) multi‐agent systems with multiple‐leader leader–follower structure is studied, in this paper. It is assumed that the communication data of the agents are noisy, and faults may occur in either leaders or followers. The followers are not aware of the main trajectory and just follow the leaders. The first step in this paper is to reduce the communication noise. Besides, if a fault occurs in one of the leaders causing to exit the leader from the main trajectory, the followers should be able to detect the fault, isolate the leader, use the other leaders' data, and keep the main trajectory. Tracking the trajectory by followers in such a case is the second step of this paper. Preserving the time‐varying formation despite a fault occurring in one of the followers is the next step. To reach these goals, a distributed array of Kalman filters is used for noise cancellation and data extraction. Next, the combination of state vector data fusion and some type of 2 test are employed for DFDI, in agents. Besides, a controller is implemented in each agent for formation tracking. In this controller, estimated relative position and velocity vectors of the agents are employed to keep the time varying formation while tracking the leaders. The closed‐loop stability is studied via Lyapunov stability theorem, and various simulations are carried out to demonstrate the capability of the proposed strategy.

Spatio-temporal data fusion for fine-resolution subsidence estimation

Land subsidence provides important information about the spatial and temporal changes occurring in the subsurface (e.g. groundwater levels, geology, etc.). However, sufficient subsidence data are difficult to obtain using only one sensor or survey, often resulting in a tradeoff between spatial resolution and temporal coverage. This study aims to estimate the high spatio-temporal resolution land subsidence by using a kernel-based vector data fusion approach between annual leveling and monthly subsidence monitoring well data, while invoking an invariant relation of subsidence information. Subsidence patterns and processes can be identified when spatio-temporal fusion of sensor data are implemented. In this subsidence investigation in Yunlin and Chunghua counties, Taiwan, the root mean square error (RMSE) is 0.52 cm in the fusion stage, and the mapping RMSE is 0.53 cm in the interpolation. The fused subsidence data readily show that the subsidence hotspot varies with time and space. The subsidence hotspots are in the western region during the winter (related to aquaculture activities) but move to the inland areas of Yunlin County during the following spring (related to agricultural activities). The proposed approach can help explain the spatio-temporal variability of the subsidence pattern.

Geometric Location Fusion of Marine Geographic and Ecological Environment Information

Fan, Z. and Xu, S., 2020. Geometric location fusion of marine geographic and ecological environment information. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 338–341. Coconut Creek (Florida), ISSN 0749-0208.With the implementation and development of China's marine strategy, the development and use of the ocean has become a research hotspot. However, the use of the ocean cannot be separated from the protection of marine geographic and ecological environment information, especially the geometric location information. First, this paper introduces two- and three-dimensional vector data fusion of marine geographic information. Second, it analyzes the key technologies and applications of marine geographic and ecological environment information fusion to realize the fusion of a variety of digital marine geographic environment information and to enrich the guarantee methods of geometric location information of marine geographic environment.

EFFICIENT AND ACCURATE FUSION OF MASSIVE VECTOR DATA ON 3D TERRAIN

Abstract. This paper presents a viewpoint-related fusion method of massive vector data and 3D terrain, in order to superpose the massive 2D vector data onto the undulating multi-resolution 3D terrain precisely and efficiently. First, the method establishes an adaptive hierarchical grid spatial index for vector data. It will determine the geographic spatial relationship between vector data and the tiles of 3D terrain in the visible area; secondly, this method will use the improved sub-pixel graphics engine AggExt to generate textures for vector data that has been bound to terrain tiles in real time; Finally, considering that a large amount of vector data will generate a lot of 2D textures in the computer memory, the method should release the “expired” vector textures. In this paper, in order to take into account the real-time convergence and the smooth interactivity of 3D scenes, this method will adopt a multi-threading strategy. The experimental results show that this method can realize the real-time and seamless fusion of massive vector objects on the 3D terrain, and has a high rendering frame rate. It can also reduce the aliasing produced by traditional texture-based methods and improve the quality of vector data fusion.

An Innovative Asynchronous, Multi-rate, Multi-sensor State Vector Fusion Algorithm for Air Defence Applications

Abstract This paper presents an innovative approach for data fusion of asynchronous, multi-rate, multi-sensor data in real time. This technique is based on state vector data fusion. For air defence applications, the target information from multi sensors is sent to a central control centre to create a complete threat scenario which would be used for taking appropriate decision. This target data needs to be sent in real time, hence there is a need for low bandwidth consuming data fusion algorithms but at the same time there should be minimal degradation in data accuracy. The optimal fusion algorithm requires the entire state covariance information matrix to be transmitted to the data fusion centre, however this requires huge bandwidth for transmission of data. To avoid this generally, only diagonal elements are transmitted at the cost of considerable degradation in performance. The authors present an innovative approach by considering the only covariance of position, velocity and acceleration but by neglecting the cross covariance terms among position, velocity and acceleration. The accuracy of this proposed algorithm is about 95% of that of optimal method but the reduction in bandwidth requirement is of the order of 60%.

Techniques and Methods of Spatial Data Fusion

Spatial data fusion is an important method of spatial data acquisition. The aim of multisource spatial data integration and fusion is to improve the information precision and information's utilization efficiency. Vector and raster are the two main spatial data structures. This article discusses vector data fusion from of data model fusion, semantic information fusion and coordinates unification, reviews the main methods of raster data fusion and discusses the key technologies of vector and raster data fusion, and proposes the future developments of spatial data fusion technique.