The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information b...The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.展开更多
The uncertainty of observers' positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in ...The uncertainty of observers' positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error (LMMSE) estimator. The results of computer si-mulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers' positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibra-tion, is approximating to the Cramer-Rao lower bound (CRLB).展开更多
Accurate global land cover(GLC), as a key input for scientific communities, is important for a wide variety of applications. In order to understand the current suitability and limitation of GLC products, the discrepan...Accurate global land cover(GLC), as a key input for scientific communities, is important for a wide variety of applications. In order to understand the current suitability and limitation of GLC products, the discrepancy and pixellevel uncertainty in major GLC products in three epochs are assessed in this study by using an integrated uncertainty index(IUI) that combines the thematic uncertainty and local classification accuracy uncertainty. The results show that the overall spatial agreements(Ao values) between GLC products are lower than 58%, and the total areas of forests are very consistent in major GLC products, but significant differences are found in different forest classes.The misclassification among different forest classes and mosaic types can account for about 20% of the total disagreements. The mean IUI almost reaches 0.5, and high uncertainty mostly occurs in transition zones and heterogeneous areas across the world. Further efforts are needed to make in the land cover classifications in areas with high uncertainty. Designing a classification scheme for climate models, with explicit definitions of land cover classes in the threshold of common attributes, is urgently needed. Information of the pixel-level uncertainty in major GLC products not only give important implications for the specific application, but also provide a quite important basis for land cover fusion.展开更多
文摘The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.
基金supported by the Fundamental Research Funds for the Central Universities(ZYGX2009J016)
文摘The uncertainty of observers' positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error (LMMSE) estimator. The results of computer si-mulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers' positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibra-tion, is approximating to the Cramer-Rao lower bound (CRLB).
基金the National Natural Science Foundation of China (Grant No. 60572027)the Program for New Century Excellent Talents in University of China (Grant No. NCET-05-0794)+2 种基金the Sichuan Youth Science & Technology Foundation (Grant No. 03ZQ026-033)the National Defense Pre-research Foundation of China (Grant No. 51430804QT2201)the Application Basic Foundation of Sichuan Province, China (Grant No. 2006 J13-10)
基金Supported by the National Key Research and Development Program of China(2016YFA0600303 and 2018YFC1506506)。
文摘Accurate global land cover(GLC), as a key input for scientific communities, is important for a wide variety of applications. In order to understand the current suitability and limitation of GLC products, the discrepancy and pixellevel uncertainty in major GLC products in three epochs are assessed in this study by using an integrated uncertainty index(IUI) that combines the thematic uncertainty and local classification accuracy uncertainty. The results show that the overall spatial agreements(Ao values) between GLC products are lower than 58%, and the total areas of forests are very consistent in major GLC products, but significant differences are found in different forest classes.The misclassification among different forest classes and mosaic types can account for about 20% of the total disagreements. The mean IUI almost reaches 0.5, and high uncertainty mostly occurs in transition zones and heterogeneous areas across the world. Further efforts are needed to make in the land cover classifications in areas with high uncertainty. Designing a classification scheme for climate models, with explicit definitions of land cover classes in the threshold of common attributes, is urgently needed. Information of the pixel-level uncertainty in major GLC products not only give important implications for the specific application, but also provide a quite important basis for land cover fusion.