Simple GNSS navigation receivers, developed for the mass market, can be used for positioning with sub centimeter accuracy in a wireless sensor network if the read-out of the carrier phase data is possible and all data...Simple GNSS navigation receivers, developed for the mass market, can be used for positioning with sub centimeter accuracy in a wireless sensor network if the read-out of the carrier phase data is possible and all data is permanently broadcast to a central computer for near real time processing of the respective base lines. Experiences gained in two research projects related to landslide monitoring are depicted in terms of quality and reliability of the results by the developed approach. As far as possible a modular system set up with commercial off-the-shelf components, e.g., standard WLAN fur commtmication, solar batteries with solar panels for autarkic power supply and in cooperation of existing proofed program tools is chosen. The challenge of the still ongoing development is to have a flexible and robust GNSS based sensor network available - concerned not only for landslide monitoring in future.展开更多
To realize the automatic detection of solar radio burst(SRB)intensity,detection based on a modified multifactor support vector machine(SVM)algorithm is proposed.First,the influence of SRB on global navigation satellit...To realize the automatic detection of solar radio burst(SRB)intensity,detection based on a modified multifactor support vector machine(SVM)algorithm is proposed.First,the influence of SRB on global navigation satellite system(GNSS)signals is analyzed.Feature vectors,which can reflect the SRB intensity of stations,are also extracted.SRB intensity is classified according to the solar radio flux,and different class labels correspond to different SRB intensity types.The training samples are composed of feature vectors and their corresponding class labels.Second,training samples are input into SVM classifiers to one-against-one training to obtain the optimal classification models.Finally,the optimal classification model is synthesized into a modified multifactor SVM classifier,which is used to automatically detect the SRB intensity of new data.Experimental results indicate that for historical SRB events,the average accuracy of SRB intensity detection is greater than 90%when the solar incident angle is higher than 20°.Compared with other methods,the proposed method considers many factors with higher accuracy and does not rely on radio telescopes,thereby saving cost.展开更多
文摘Simple GNSS navigation receivers, developed for the mass market, can be used for positioning with sub centimeter accuracy in a wireless sensor network if the read-out of the carrier phase data is possible and all data is permanently broadcast to a central computer for near real time processing of the respective base lines. Experiences gained in two research projects related to landslide monitoring are depicted in terms of quality and reliability of the results by the developed approach. As far as possible a modular system set up with commercial off-the-shelf components, e.g., standard WLAN fur commtmication, solar batteries with solar panels for autarkic power supply and in cooperation of existing proofed program tools is chosen. The challenge of the still ongoing development is to have a flexible and robust GNSS based sensor network available - concerned not only for landslide monitoring in future.
基金The National Key Research and Development Plan of China(No.2018YFB0505103)the National Natural Science Foundation of China(No.61873064)。
文摘To realize the automatic detection of solar radio burst(SRB)intensity,detection based on a modified multifactor support vector machine(SVM)algorithm is proposed.First,the influence of SRB on global navigation satellite system(GNSS)signals is analyzed.Feature vectors,which can reflect the SRB intensity of stations,are also extracted.SRB intensity is classified according to the solar radio flux,and different class labels correspond to different SRB intensity types.The training samples are composed of feature vectors and their corresponding class labels.Second,training samples are input into SVM classifiers to one-against-one training to obtain the optimal classification models.Finally,the optimal classification model is synthesized into a modified multifactor SVM classifier,which is used to automatically detect the SRB intensity of new data.Experimental results indicate that for historical SRB events,the average accuracy of SRB intensity detection is greater than 90%when the solar incident angle is higher than 20°.Compared with other methods,the proposed method considers many factors with higher accuracy and does not rely on radio telescopes,thereby saving cost.
