Real-time mobility tracking algorithms for cellular networks based on Kalman filtering

2005 ◽  
Vol 4 (2) ◽  
pp. 195-208 ◽  
Author(s):  
Z.R. Zaidi ◽  
B.L. Mark
Keyword(s):  
Real Time ◽  
Cellular Networks ◽  
Kalman Filtering ◽  
Tracking Algorithms ◽  
Mobility Tracking

scholarly journals Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Pengpeng Jiao ◽  
Ruimin Li ◽  
Tuo Sun ◽  
Zenghao Hou ◽  
Amir Ibrahim
Keyword(s):  
Real Time ◽  
Nonparametric Regression ◽  
Kalman Filtering ◽  
Rail Transit ◽  
Correction Coefficient ◽  
Short Term ◽  
Transit System ◽  
Filtering Method ◽  
Passenger Flow ◽  
Model Based

Short-term prediction of passenger flow is very important for the operation and management of a rail transit system. Based on the traditional Kalman filtering method, this paper puts forward three revised models for real-time passenger flow forecasting. First, the paper introduces the historical prediction error into the measurement equation and formulates a revised Kalman filtering model based on error correction coefficient (KF-ECC). Second, this paper employs the deviation between real-time passenger flow and corresponding historical data as state variable and presents a revised Kalman filtering model based on Historical Deviation (KF-HD). Third, the paper integrates nonparametric regression forecast into the traditional Kalman filtering method using a Bayesian combined technique and puts forward a revised Kalman filtering model based on Bayesian combination and nonparametric regression (KF-BCNR). A case study is implemented using statistical passenger flow data of rail transit line 13 in Beijing during a one-month period. The reported prediction results show that KF-ECC improves the applicability to historical trend, KF-HD achieves excellent accuracy and stability, and KF-BCNR yields the best performances. Comparisons among different periods further indicate that results during peak periods outperform those during nonpeak periods. All three revised models are accurate and stable enough for on-line predictions, especially during the peak periods.

Visual Hysteresis Based Dynamic Interference Shaping for Real-Time Video Services in Dense Deployed Cellular Networks

2016 ◽  
Vol 96 (4) ◽  
pp. 5221-5238
Author(s):  
Zhaoming Lu ◽  
Shiyu Zhou ◽  
Xiangming Wen ◽  
Hua Shao ◽  
Bin Fu
Keyword(s):  
Real Time ◽  
Cellular Networks ◽  
Video Services ◽  
Dynamic Interference
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