Evaluation of TDOA based Football Player’s Position Tracking Algorithm using Kalman Filter

• Main Authors: Kanduri, Srinivasa Rangarajan Mukhesh, Medapati, Vinay Kumar Reddy
• Format: Others
• Language: English
• Published: Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling 2018
• Subjects: Gauss Newton method; Player Localization; Radio transmission; Single Point Localization; TDOA; Kalman filter; Wireless Sensor Network; Signal Processing; Signalbehandling
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16433

Time Difference Of Arrival (TDOA) based position tracking technique is one of the pinnacles of sports tracking technology. Using radio frequency com-munication, advanced filtering techniques and various computation methods, the position of a moving player in a virtually created sports arena can be iden-tified using MATLAB. It can also be related to player’s movement in real-time. For football in particular, this acts as a powerful tool for coaches to enhanceteam performance. Football clubs can use the player tracking data to boosttheir own team strengths and gain insight into their competing teams as well. This method helps to improve the success rate of Athletes and clubs by analyz-ing the results, which helps in crafting their tactical and strategic approach to game play. The algorithm can also be used to enhance the viewing experienceof audience in the stadium, as well as broadcast.In this thesis work, a typical football field scenario is assumed and an arrayof base stations (BS) are installed along perimeter of the field equidistantly.The player is attached with a radio transmitter which emits radio frequencythroughout the assigned game time. Using the concept of TDOA, the position estimates of the player are generated and the transmitter is tracked contin-uously by the BS. The position estimates are then fed to the Kalman filter, which filters and smoothens the position estimates of the player between the sample points considered. Different paths of the player as straight line, circu-lar, zig-zag paths in the field are animated and the positions of the player are tracked. Based on the error rate of the player’s estimated position, the perfor-mance of the Kalman filter is evaluated. The Kalman filter’s performance is analyzed by varying the number of sample points.