Many indoor fingerprinting localization methods are based on signal-domain distances with large localization error and low stability. An improved fingerprinting localization method using a clustering algorithm and dynamic compensation was proposed. In the offline stage, the fingerprint database was built and clustered based on offline hybrid distance and an affinity propagation clustering algorithm. Furthermore, clusters were adjusted using transition regions and a given radius, as well as updating the corresponding position and fingerprint of the cluster centroid. In the online stage, the lost received signal strength (RSS) in the reference fingerprint would be dynamically compensated by using a minimum RSS value, rather than a fixed one. Online signal-domain distance was calculated for cluster identification based on RSS readings and compensated reference fingerprint. Then, K reference points with minimum online signal-domain distances were selected, and affinity propagation clustering was reused by position-domain distances to choose the position-concentrated sub-cluster for location estimation. Experimental results show that the proposed method outperforms state-of-the-art fingerprinting methods, with the mean error of 2.328 m, the root mean square error of 1.865 m and the maximum error of 10.722 m in a testbed of 3200 square meters. The improvement rates, in terms of accuracy and stability, are more than 21% and 13%, respectively.
It is well-known that structures composed of super high-rise buildings accumulate damages gradually due to ultra-long loads, material aging, and component defects. Thus, the bearing capacity of the structures can be significantly decreased. In addition, these effects may cause inestimable life and property losses upon strong winds, earthquakes, and other heavy loads. Hence, it is necessary to develop real-time health monitoring methods for super high-rise buildings to deeply understand the running state during operation, timely discover potential safety potentials, and to provide reference data for reinforcement design. Along these lines, in this work, the built super high-rise buildings (Yunding Building) and super high-rise buildings (the Main Tower of the Shandong International Financial Center), under construction, were selected as the research objects. The overall dynamic deformation laws of super high-rise buildings were monitored by using ground-based real aperture radar (GB-RAR) technology for its advantages in non-contact measurement, remote monitoring, and real-time display of observation results. Denoising of the observation data was also carried out based on wavelet analysis. The visualization of the space state of the Yunding Building was realized based on handheld LiDAR technology. From the acquired results, it was demonstrated that the measuring accuracy of GB-RAR could reach the submillimeter level, while the noises under a natural state of wavelet analysis were eliminated well. The maximum deformation values of the Yunding Building and the Main Tower of Shandong International Financial Center under their natural state were 9.63 mm and 16.46 mm, respectively. Under sudden wind loads, the maximum deformation of the Yunding Building could be as high as 895.79 mm. The overall motion state switched between an S-shaped pattern, hyperbolic-type, and oblique line, presented the characteristics of nonlinear elastic deformation.
Abstract-Mapping control survey for 13 control reference points were carried out based on RTK technology using GPS/BEIDOU/GLONASS multi-constellation compatible GNSS receivers in various urban environment (open, building shelter, tree shading, etc.). Operation scheme and technique flow were discussed. Operation efficiency and positioning accuracy in different environment were analyzed. Experimental results show that the horizontal position and elevation of the points measured by RTK can reach 2cm and 3cm precision level respectively in open environment, which can meet the accuracy requirements for mapping horizontal and vertical control survey. However in environment such as building shelter or tree shading, the horizontal position of the points measured by RTK can reach 2cm precision level generally, while the elevation biases are more than 3cm mostly, which is difficult to achieve the accuracy of elevation mapping control survey.
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