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Jianping Xing
School of Microelectronics, Shandong University, Jinan 250101, China

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Journal article
Published: 24 December 2020 in Applied Sciences
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The timing group delay parameter (TGD) or differential code bias parameter (DCB) is an important factor that affects the performance of GNSS basic services; therefore, TGD and DCB must be taken seriously. Moreover, the TGD parameter is modulated in the navigation message, taking into account the impact of TGD on the performance of the basic service. International GNSS Monitoring and Assessment System (iGMAS) provides the broadcast ephemeris with TGD parameter and the Chinese Academy of Science (CAS) provides DCB products. In this paper, the current available BDS-3 TGD and DCB parameters are firstly described in detail, and the relationship of TGD and DCB for BDS-3 is figured out. Then, correction models of BDS-3 TGD and DCB in standard point positioning (SPP) or precise point positioning (PPP) are given, which can be applied in various situations. For the effects of TGD and DCB in the SPP and PPP solution processes, all the signals from BDS-3 were researched, and the validity of TGD and DCB has been further verified. The experimental results show that the accuracy of B1I, B1C and B2a single-frequency SPP with TGD or DCB correction was improved by approximately 12–60%. TGD will not be considered for B3I single-frequency, because the broadcast satellite clock offset is based on the B3I as the reference signal. The positioning accuracy of B1I/B3I and B1C/B2a dual-frequency SPP showed that the improvement range for horizontal components is 60.2% to 74.4%, and the vertical components improved by about 50% after the modification of TGD and DCB. In addition, most of the uncorrected code biases are mostly absorbed into the receiver clock bias and other parameters for PPP, resulting in longer convergence time. The convergence time can be max increased by up to 50% when the DCB parameters are corrected. Consequently, the positioning accuracy can reach the centimeter level after convergence, but it is critical for PPP convergence time and receiver clock bias that the TGD and DCB correction be considered seriously.

ACS Style

Peipei Dai; Jianping Xing; Yulong Ge; Xuhai Yang; Weijin Qin; Yanchen Dong; Zhe Zhang. The Effect of BDS-3 Time Group Delay and Differential Code Bias Corrections on Positioning. Applied Sciences 2020, 11, 104 .

AMA Style

Peipei Dai, Jianping Xing, Yulong Ge, Xuhai Yang, Weijin Qin, Yanchen Dong, Zhe Zhang. The Effect of BDS-3 Time Group Delay and Differential Code Bias Corrections on Positioning. Applied Sciences. 2020; 11 (1):104.

Chicago/Turabian Style

Peipei Dai; Jianping Xing; Yulong Ge; Xuhai Yang; Weijin Qin; Yanchen Dong; Zhe Zhang. 2020. "The Effect of BDS-3 Time Group Delay and Differential Code Bias Corrections on Positioning." Applied Sciences 11, no. 1: 104.

Journal article
Published: 22 November 2019 in Future Internet
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Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

ACS Style

Xin Zhou; Peixin Dong; Jianping Xing; Peijia Sun. Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network. Future Internet 2019, 11, 247 .

AMA Style

Xin Zhou, Peixin Dong, Jianping Xing, Peijia Sun. Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network. Future Internet. 2019; 11 (12):247.

Chicago/Turabian Style

Xin Zhou; Peixin Dong; Jianping Xing; Peijia Sun. 2019. "Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network." Future Internet 11, no. 12: 247.

Journal article
Published: 15 April 2019 in Future Internet
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Aiming at the problems of poor time performance and accuracy in bus stops network optimization, this paper proposes an algorithm based on complex network and graph theory and Beidou Vehicle Location to measure the importance of bus stops. This method narrows the scope of points and edges to be optimized and is applied to the Jinan bus stop network. In this method, the bus driving efficiency, which can objectively reflect actual road conditions, is taken as the weight of the connecting edges in the network, and the network is optimized through the network efficiency. The experimental results show that, compared with the original network, the optimized network time performance is good and the optimized network bus driving efficiency is improved.

ACS Style

Peixin Dong; Dongyuan Li; Jianping Xing; Haohui Duan; Yong Wu. A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location. Future Internet 2019, 11, 97 .

AMA Style

Peixin Dong, Dongyuan Li, Jianping Xing, Haohui Duan, Yong Wu. A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location. Future Internet. 2019; 11 (4):97.

Chicago/Turabian Style

Peixin Dong; Dongyuan Li; Jianping Xing; Haohui Duan; Yong Wu. 2019. "A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location." Future Internet 11, no. 4: 97.

Journal article
Published: 24 October 2017 in Future Internet
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In this paper, we propose a consensus algorithm with input constraints for traffic light signals in transit signal priority (TSP). TSP ensures control strategy of traffic light signals can be adjusted and applied according to the real-time traffic status, and provides priority for buses. We give the convergence conditions of the consensus algorithms with and without input constraints in TSP respectively and analyze the convergence performance of them by using matrix theory and graph theory, and PTV-VISSIM is used to simulate the traffic accident probability of three cases at intersections. Simulation results are presented that a consensus is asymptotically reached for all weights of priority; the algorithm with input constraints is more suitable for TSP than the algorithm without input constraints, and the traffic accident rate is reduced.

ACS Style

Dongyuan Li; Chengshuai Li; Zidong Wang; Deqiang Wang; Jianping Xing; Bo Zhang. Signal Consensus in TSP of the Same Grid in Road Network. Future Internet 2017, 9, 69 .

AMA Style

Dongyuan Li, Chengshuai Li, Zidong Wang, Deqiang Wang, Jianping Xing, Bo Zhang. Signal Consensus in TSP of the Same Grid in Road Network. Future Internet. 2017; 9 (4):69.

Chicago/Turabian Style

Dongyuan Li; Chengshuai Li; Zidong Wang; Deqiang Wang; Jianping Xing; Bo Zhang. 2017. "Signal Consensus in TSP of the Same Grid in Road Network." Future Internet 9, no. 4: 69.

Book chapter
Published: 01 January 2012 in Advances in Intelligent and Soft Computing
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The diagonal-matrix-weight IMM (DIMM) algorithm can solve the IMM algorithm confusions of probability density functions (PDFs) and probability masses of stochastic process. However, the DIMM algorithm may generate calculating divergence, caused by the fact that the state error covariance matrix is repeatedly applied to calculate the inverse matrix. A new method, LDU factorization diagonal-matrix-weight IMM (LDU-DIMM) algorithm, is proposed in this paper to solve the factorization of asymmetric matrix error covariance of DIMM algorithm. Experiment results verify the effectiveness of the proposed algorithm.

ACS Style

Liang Gao; Jianping Xing; Fan Shi; Zhenliang Ma; Junchen Sha; Juan Sun. The Improved Diagonal-Matrix-Weight IMM Algorithm Based on LDU Factorization. Advances in Intelligent and Soft Computing 2012, 13 -21.

AMA Style

Liang Gao, Jianping Xing, Fan Shi, Zhenliang Ma, Junchen Sha, Juan Sun. The Improved Diagonal-Matrix-Weight IMM Algorithm Based on LDU Factorization. Advances in Intelligent and Soft Computing. 2012; ():13-21.

Chicago/Turabian Style

Liang Gao; Jianping Xing; Fan Shi; Zhenliang Ma; Junchen Sha; Juan Sun. 2012. "The Improved Diagonal-Matrix-Weight IMM Algorithm Based on LDU Factorization." Advances in Intelligent and Soft Computing , no. : 13-21.