排序方式： 共有7条查询结果，搜索用时 15 毫秒
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M. R. Mosavi 《GPS Solutions》2006,10(2):97-107
Position information obtained from standard global positioning system (GPS) receivers has time variant errors. For effective use of GPS information in a navigation system, it is essential to model these errors. A new approach is presented for improving positioning accuracy using neural network (NN), fuzzy neural network (FNN), and Kalman filter (KF). These methods predict the position components’ errors that are used as differential GPS (DGPS) corrections in real-time positioning. Method validity is verified with experimental data from an actual data collection, before and after selective availability (SA) error. The result is a highly effective estimation technique for accurate positioning, so that positioning accuracy is drastically improved to less than 0.40 m, independent of SA error. The experimental test results with real data emphasize that the total performance of NN is better than FNN and KF considering the trade-off between accuracy and speed for DGPS corrections prediction. 相似文献
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Mohamadi Sedigheh Sammen Saad Sh. Panahi Fatemeh Ehteram Mohammad Kisi Ozgur Mosavi Amir Ahmed Ali Najah El-Shafie Ahmed Al-Ansari Nadhir 《Natural Hazards》2020,104(1):537-579
Natural Hazards - The modelling of drought is of utmost importance for the efficient management of water resources. This article used the adaptive neuro-fuzzy interface system (ANFIS), multilayer... 相似文献
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A new method to mitigate multipath error in single-frequency GPS receiver with wavelet transform 总被引：2，自引：1，他引：1
One of the major errors in high-precision GPS positioning is multipath. Multipath effect modeling and reduction have been a challenging issue in high-accuracy GPS positioning applications due to its special properties. Different methods have been employed to mitigate this error including hardware and software approaches. We reduce C/A code multipath error by adopting an efficient software method which uses wavelet transform as a basic data processing trend. The key idea of the proposed method is using stationary wavelet transform (SWT) in GPS signal data processing. Since we have used SWT, there is complete access to high-frequency and low-frequency terms in both time and frequency domains, and we can apply appropriated procedures to mitigate this error. The multipath error mostly is a low-frequency term. In our proposed method, the double difference (DD) residuals are applied to the SWT to identify the multipath disturbance. The extracted multipath is then used to correct DD observations. Our experiments include three data sets to investigate the proposed method and compare it with existing algorithms. We used simulations for two of these data sets. The results indicate the efficiency of the proposed method over existing algorithms. 相似文献
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A wavelet-extreme learning machine for low-cost INS/GPS navigation system in high-speed applications
The combined navigation system consisting of both global positioning system (GPS) and inertial navigation system (INS) results in reliable, accurate, and continuous navigation capability when compared to either a GPS or an INS stand-alone system. To improve the overall performance of low-cost micro-electro-mechanical systems (MEMS)-based INS/GPS by considering a high level of stochastic noise on low-cost MEMS-based inertial sensors, a highly complex problems with noisy real data, a high-speed vehicle, and GPS signal outage during our experiments, we suggest two approaches at different steps: (1) improving the signal-to-noise ratio of the inertial sensor measurements and attenuating high-frequency noise using the discrete wavelet transform technique before data fusion while preserving important information like the vehicle motion information and (2) enhancing the positioning accuracy and speed by an extreme learning machine (ELM) which has the characteristics of quick learning speed and impressive generalization performance. We present a single-hidden layer feedforward neural network which is employed to optimize the estimation accuracy and speed by minimizing the error, especially in the high-speed vehicle and real-time implementation applications. To validate the performance of our proposed method, the results are compared with an adaptive neuro-fuzzy inference system (ANFIS) and an extended Kalman filter (EKF) method. The achieved accuracies are discussed. The results suggest a promising and superior prospect for ELM in the field of positioning for low-cost MEMS-based inertial sensors in the absence of GPS signal, as it outperforms ANFIS and EKF by approximately 50 and 70%, respectively. 相似文献
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GPS Solutions - In traditional federated receiver, all the tracking channels work independently, and there is no interaction among them. However, in vectorized receiver, stronger channels aid... 相似文献
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Sadra Shadkani Akram Abbaspour Saeed Samadianfard Sajjad Hashemi Amirhosein Mosavi Shahab S.Band 《国际泥沙研究》2021,36(4):512-523
Monitoring sediment transport is essential for managing and maintaining rivers.Estimation of the sediment load in rivers is fundamental for the study of sediment movement,erosion,and flood control.In the current study,three machine learning models-multi-layer perceptron(MLP),multi-layer perceptron-stochastic gradient descent(MLP-SGD),and gradient boosted tree(GBT)-were utilized to estimate the suspended sediment load(SSL)at the St.Louis(SL)and Chester(CH)stations on the Mississippi River,U.S.Four evaluation criteria including the Correlation Coefficient(CC),Nash Sutcliffe Efficiency(NSE),Scatter Index(SI),and Willmott’s Index(WI)were utilized to evaluate the performance of the used models.A sensitivity analysis of the models to the input variables revealed that the current day discharge variable had the most effect on the SSL at both stations,but in the absence of current-day discharge data(Qt),a combination of input parameters including SSLt-3,SSLt-2,SSLt-1,Qt-3,Qt-2,Qt-1 can be used to estimate the SSL.The comparative outcomes indicated the high accuracy of MLP-SGD-5 model with a CC of 0.983,SI of 0.254,WI of 0.991,and NSE of 0.967 at station CH and the MLP-SGD-6 model with a CC of 0.933,SI of 0.576,WI of 0.961,and NSE of 0.867,respectively,at station SL.The results of MLP models were improved by SGD optimization.Therefore,the MLP-SGD method is recommended as the most accurate model for SSL estimation. 相似文献
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