Multi-channel Global Positioning System (GPS) satellite signal simulator is used to provide realistic test signals for GPS receivers and navigation systems. In this paper, signals arriving the antenna of GPS receiver ...Multi-channel Global Positioning System (GPS) satellite signal simulator is used to provide realistic test signals for GPS receivers and navigation systems. In this paper, signals arriving the antenna of GPS receiver are analyzed from the viewpoint of simulator design. The estimation methods are focused of which several signal parameters are difficult to determine directly according to existing experiential models due to various error factors. Based on the theory of Artificial Neural Network (ANN), an approach is proposed to simulate signal propagation delay,carrier phase, power, and other parameters using ANN. The architecture of the hardware-in-the-loop test system is given. The ANN training and validation process is described. Experimental results demonstrate that the ANN designed can statistically simulate sample data in high fidelity.Therefore the computation of signal state based on this ANN can meet the design requirement,and can be directly applied to the development of multi-channel GPS satellite signal simulator.展开更多
Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many s...Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks （clusters） so that channel interferences and flooding messages can be limited. This research presents a novel Multi-Resolution Relative Speed Detection （MRSD） model to improve the clustering algorithm in VANET without using Global Positioning System （GPS）. MRSD uses the Moving Average Convergence Divergence （MACD）, the Momentum of Received Signal Strength （MRSS）, and Artificial Neural Networks （ANNs） to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.展开更多
WLAN mdoor location method based on artificial neural network （ANN） is analyzed. A three layer feed-forward ANN model offers the benefits of reducing time cost of the layout of an indoor location system, saving stor...WLAN mdoor location method based on artificial neural network （ANN） is analyzed. A three layer feed-forward ANN model offers the benefits of reducing time cost of the layout of an indoor location system, saving storage cost of the radio map establishment and enhancing real-time capacity in the on-line phase. According to the analysis of SNR distributions of recorded beacon signal samples and discussion about the multi-mode phenomenon, the one map method is proposed for the purpose of simplifying ANN input values and increasing location performances. Based on the simulations and comparison analysis with other two typical indoor location methods, K-nearest neighbor （KNN） and probability, the feasibility and effectiveness of ANN-based indoor location method are verified with average location error of 2.37m and location accuracy of 78.6% in 3m.展开更多
基金Supported by the National Natural Science Foundation of China (No.60027001).
文摘Multi-channel Global Positioning System (GPS) satellite signal simulator is used to provide realistic test signals for GPS receivers and navigation systems. In this paper, signals arriving the antenna of GPS receiver are analyzed from the viewpoint of simulator design. The estimation methods are focused of which several signal parameters are difficult to determine directly according to existing experiential models due to various error factors. Based on the theory of Artificial Neural Network (ANN), an approach is proposed to simulate signal propagation delay,carrier phase, power, and other parameters using ANN. The architecture of the hardware-in-the-loop test system is given. The ANN training and validation process is described. Experimental results demonstrate that the ANN designed can statistically simulate sample data in high fidelity.Therefore the computation of signal state based on this ANN can meet the design requirement,and can be directly applied to the development of multi-channel GPS satellite signal simulator.
文摘Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks （clusters） so that channel interferences and flooding messages can be limited. This research presents a novel Multi-Resolution Relative Speed Detection （MRSD） model to improve the clustering algorithm in VANET without using Global Positioning System （GPS）. MRSD uses the Moving Average Convergence Divergence （MACD）, the Momentum of Received Signal Strength （MRSS）, and Artificial Neural Networks （ANNs） to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.
文摘WLAN mdoor location method based on artificial neural network （ANN） is analyzed. A three layer feed-forward ANN model offers the benefits of reducing time cost of the layout of an indoor location system, saving storage cost of the radio map establishment and enhancing real-time capacity in the on-line phase. According to the analysis of SNR distributions of recorded beacon signal samples and discussion about the multi-mode phenomenon, the one map method is proposed for the purpose of simplifying ANN input values and increasing location performances. Based on the simulations and comparison analysis with other two typical indoor location methods, K-nearest neighbor （KNN） and probability, the feasibility and effectiveness of ANN-based indoor location method are verified with average location error of 2.37m and location accuracy of 78.6% in 3m.
