Current Issue : October - December Volume : 2018 Issue Number : 4 Articles : 5 Articles
This paper presents a new Strap-down Inertial Navigation System/Spectrum Red-Shift/Star\nSensor (SINS/SRS/SS) system integration methodology to improve the autonomy and reliability of\nspacecraft navigation using the spectrum red-shift information from natural celestial bodies such\nas the Sun, Jupiter and the Earth. The system models for SINS/SRS/SS integration are established.\nThe information fusion of SINS/SRS/SS integration is designed as the structure of the federated\nKalman filter to fuse the local estimations of SINS/SRS and SINS/SS integrated subsystems to\ngenerate the global state estimation for spacecraft navigation. A new robust adaptive unscented\nparticle filter is also developed to obtain the local state estimations of SINS/SRS and SINS/SS\nintegrated subsystems in a parallel manner. The simulation results demonstrate that the proposed\nmethodology for SINS/SRS/SS integration can effectively calculate navigation solutions, leading to\nstrong autonomy and high reliability for spacecraft navigation....
There are many tasks that require clear and easily recognizable images in the field of\nunderwater robotics and marine science, such as underwater target detection and identification of\nrobot navigation and obstacle avoidance. However, water turbidity makes the underwater image\nquality too low to recognize. This paper proposes the use of the dark channel prior model for\nunderwater environment recognition, in which underwater reflection models are used to obtain\nenhanced images. The proposed approach achieves very good performance and multi-scene\nrobustness by combining the dark channel prior model with the underwater diffuse model.\nThe experimental results are given to show the effectiveness of the dark channel prior model in\nunderwater scenarios....
The application of high-resolution imagery from unmanned aerial vehicles (UAV) to\nclassify the spatial extent and morphological character of ground and polished stone tool production\nat quarry sites in the Shetland Islands is explored in this paper. These sites are manifest as dense\nconcentrations of felsite and artefacts clearly visible on the surface of the landscape. Supervised\nclassification techniques are applied to map material extents in detail, while a topological analysis of\nsurface rugosity derived from an image-based modelling (IBM) generated high-resolution elevation\nmodel is used to remotely assess the size and morphology of the material. While the approach is\nunable to directly characterize felsite as debitage, it successfully captured size and morphology, key\nindicators of archaeological activity. It is proposed that the classification of red, green and blue (RGB)\nimagery and rugosity analysis derived from IBM from UAV collected photographs can remotely\nprovide data on stone quarrying processes and can act as an invaluable decision support tool for\nmore detailed targeted field characterisation, especially on large sites where material is spread over\nwide areas. It is suggested that while often available, approaches like this are largely under-utilized,\nand there is considerable added value to be gained from a more in-depth study of UAV imagery and\nderived datasets....
In recent years, researches of disseminating wireless network have been conducted\nfor areas without network infrastructure such as disaster situation or\nmilitary disputes. However, conventional method was to provide a communication\ninfrastructure by floating large aircraft as UAV or hot-air balloon in\nthe high air. Therefore, it was difficult to utilize previous method because it\nrequires a lot of time and cost. But it is possible to save money and time by\nusing a drone which is already used in many areas as a small UAV. In this paper,\nwe design a drone that can provide wireless infrastructure using high\nspeed Wi-Fi. After reaching the target area, the drone can provide Wi-Fi using\nwireless mesh network and transmit the situation of local area through attached\ncamera. And the transmitted videos can be monitored in the control\ncenter or cell phone through application in real time. The proposed scheme\nprovides wireless communication of up to 160 Mbps in a coverage of about\n200 m and video transmission with a coverage of about 120 m, respectively....
Unmanned aerial vehicles (UAV) are being used for low altitude remote sensing for\nthematic land classification using visible light and multi-spectral sensors. The objective of this\nwork was to investigate the use of UAV equipped with a compact spectrometer for land cover\nclassification. The UAV platform used was a DJI Flamewheel F550 hexacopter equipped with GPS\nand Inertial Measurement Unit (IMU) navigation sensors, and a Raspberry Pi processor and camera\nmodule. The spectrometer used was the FLAME-NIR, a near-infrared spectrometer for hyperspectral\nmeasurements. RGB images and spectrometer data were captured simultaneously. As spectrometer\ndata do not provide continuous terrain coverage, the locations of their ground elliptical footprints\nwere determined from the bundle adjustment solution of the captured images. For each of the\nspectrometer ground ellipses, the land cover signature at the footprint location was determined\nto enable the characterization, identification, and classification of land cover elements. To attain a\ncontinuous land cover classification map, spatial interpolation was carried out from the irregularly\ndistributed labeled spectrometer points. The accuracy of the classification was assessed using spatial\nintersection with the object-based image classification performed using the RGB images. Results\nshow that in homogeneous land cover, like water, the accuracy of classification is 78% and in mixed\nclasses, like grass, trees and manmade features, the average accuracy is 50%, thus, indicating the\ncontribution of hyperspectral measurements of low altitude UAV-borne spectrometers to improve\nland cover classification....
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