The theoretical positioning accuracy of multilateration(MLAT) with the time difference of arrival(TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location ...The theoretical positioning accuracy of multilateration(MLAT) with the time difference of arrival(TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location performance of the time sum of arrival(TSOA) algorithm from the root mean square error(RMSE) and geometric dilution of precision(GDOP) in additive white Gaussian noise(AWGN) environment. The TSOA localization model is constructed. Using it, the distribution of location ambiguity region is presented with 4-base stations. And then, the location performance analysis is started from the 4-base stations with calculating the RMSE and GDOP variation. Subsequently, when the location parameters are changed in number of base stations, base station layout and so on, the performance changing patterns of the TSOA location algorithm are shown. So, the TSOA location characteristics and performance are revealed. From the RMSE and GDOP state changing trend, the anti-noise performance and robustness of the TSOA localization algorithm are proved. The TSOA anti-noise performance will be used for reducing the blind-zone and the false location rate of MLAT systems.展开更多
The conflation of geographic datasets is one of the key technologies in the realm of spatial data capture and integration in geographic information system(GIS).Map conflation is a complex process of matching and mergi...The conflation of geographic datasets is one of the key technologies in the realm of spatial data capture and integration in geographic information system(GIS).Map conflation is a complex process of matching and merging spatial data.Due to various reasons such as errors in original data related to map data discrepancies,a great amount of uncertainties exists during the process and this will result in errors in featuring matching,especially point feature.Thus,it is vital to develop the method to detect the errors in feature matching and further the conflation results will not be affected.In this paper,error matching detection and robust estimation adjustment methods are proposed for map conflation.The characteristics of errors in feature matching are first analyzed,then a new approach for map conflation based on the least-squares adjustment is presented,and a robust estimation adjustment method is further proposed to detect and process matching errors.The results of the map conflation test show that the proposed method not only determines the errors in feature matching,but also obtains the optimal merging results in map conflation.展开更多
基金supported by the Joint Civil Aviation Fund of National Natural Science Foundation of China(Nos.U1533108 and U1233112)
文摘The theoretical positioning accuracy of multilateration(MLAT) with the time difference of arrival(TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location performance of the time sum of arrival(TSOA) algorithm from the root mean square error(RMSE) and geometric dilution of precision(GDOP) in additive white Gaussian noise(AWGN) environment. The TSOA localization model is constructed. Using it, the distribution of location ambiguity region is presented with 4-base stations. And then, the location performance analysis is started from the 4-base stations with calculating the RMSE and GDOP variation. Subsequently, when the location parameters are changed in number of base stations, base station layout and so on, the performance changing patterns of the TSOA location algorithm are shown. So, the TSOA location characteristics and performance are revealed. From the RMSE and GDOP state changing trend, the anti-noise performance and robustness of the TSOA localization algorithm are proved. The TSOA anti-noise performance will be used for reducing the blind-zone and the false location rate of MLAT systems.
基金the National Natural Science Foundation of China(Grant Nos.40771174 and 40301043)the Doctoral Program of Higher Education of China(Grant No.20070247046)+1 种基金the Program for ShuGuang Scholarship of Shanghai(Grant No.07SG24)Foundation of Shanghai Ris-ing-Star Program(Grant No.05QMX1456)
文摘The conflation of geographic datasets is one of the key technologies in the realm of spatial data capture and integration in geographic information system(GIS).Map conflation is a complex process of matching and merging spatial data.Due to various reasons such as errors in original data related to map data discrepancies,a great amount of uncertainties exists during the process and this will result in errors in featuring matching,especially point feature.Thus,it is vital to develop the method to detect the errors in feature matching and further the conflation results will not be affected.In this paper,error matching detection and robust estimation adjustment methods are proposed for map conflation.The characteristics of errors in feature matching are first analyzed,then a new approach for map conflation based on the least-squares adjustment is presented,and a robust estimation adjustment method is further proposed to detect and process matching errors.The results of the map conflation test show that the proposed method not only determines the errors in feature matching,but also obtains the optimal merging results in map conflation.
