Performance analysis of a non-homogeneous GPS system under Markov modulated fluid traffic

Thanks to the strong growth of MEMS technology, the Inertial Navigation System (INS) is widely applied to navigation and guidance of moving objects. However, there exist errors in the inertial sensor’s signals that cause unacceptable drifts. To minimize these effects on the INS system, a GPS is usually employed simultaneously with an INS in order to increase the dimension of the system; the desired parameters are estimated by Kalman filtering technique applied to the enlarged system. In this paper, we present the design, simulation and performance analysis of an INS/GPS system using two parallel Kaman filters in order to increase the accuracy of the parameter estimation process. The results show that this system could be efficiently brought to practical applications.

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Validation of a Video-Based Performance Analysis System (Mediacoach®) to Analyze the Physical Demands during Matches in LaLiga

Sensors ◽

10.3390/s19194113 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4113 ◽

Cited By ~ 9

Author(s):

Jose Luis Felipe ◽

Jorge Garcia-Unanue ◽

David Viejo-Romero ◽

Archit Navandar ◽

Javier Sánchez-Sánchez

Keyword(s):

Performance Analysis ◽

Real Time ◽

Tracking System ◽

Maximum Speed ◽

Camera Tracking ◽

Total Distance ◽

Maximum Bias ◽

The Difference ◽

Analysis System ◽

Gps System

The aim of the present study was to assess the accuracy of a multi-camera tracking system (Mediacoach®) to track elite football players’ movements in real time. A total of 207 observations of 38 official matches from Liga 1, 2, 3™ (2nd Spanish Division, season 2017/18) were included in the study (88 defenders, 84 midfielders, and 35 attackers of the same team). Total distance (TD, m) distance in zone 4 (DZ4) at a speed of 14–21 km/h, distance in zone 5 (DZ5) at a speed of 21–24 km/h (DZ5), distance in zone 6 (DZ6) at a speed of ≥24 km/h, maximum speed (km/h), and number of sprints (actions above 24 km/h) were registered with the Apex® GPS system (STATSports™, Newry, N. Ireland) and Mediacoach® semi-automatic tracking system (LaLiga™, Madrid, Spain). The level of agreement between variables estimated by the two systems was analyzed. Bias was also calculated by deducting the GPS estimated value from the video estimated value, and then dividing the difference score by the GPS estimated value. All variables showed high ICC values (>0.75) and very large correlations (r > 0.70). However the video-based performance analysis system overestimated the results obtained in the different speed zones (DZ5: +16.59 ± 62.29 m; LOA95%: −105.49 to 138.68; DZ6: +93.26 ± 67.76 m; LOA95%: −39.55 to 226.07), the number of sprints (+2.27 ± 2.94; LOA95%: −3.49 to 8.02), and the maximum speed (+0.32 ± 1.25 km/h; LOA95%: −2.13 to 2.77). The maximum bias was found in DZ6 (47%). This demonstrates that Mediacoach® is as accurate as a GPS system to obtain objective data in real time, adapted to physical and movement demands of elite football, especially for total distance and distances traveled at medium speeds.

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Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis

Journal of Electrical Engineering ◽

10.2478/v10187-010-0053-0 ◽

2010 ◽

Vol 61 (6) ◽

pp. 341-349 ◽

Cited By ~ 4

Author(s):

Dimitar Radev ◽

Izabella Lokshina

Keyword(s):

Performance Analysis ◽

Network Traffic ◽

Traffic Simulation ◽

Traffic Data ◽

Fractal Models ◽

Wide Range ◽

Ip Network ◽

Self Similar ◽

Markov Modulated ◽

And Performance

Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis The paper examines self-similar (or fractal) properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced fractal models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behavior of IP network traffic data are developed and applied. Numerical examples are provided; and simulation results are obtained and analyzed.

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