Optimization of Camera and Radar Placement for Sensor Fusion and Ball Tracking in Sports

The placement of sensors in an environment can significantly impact the sensing performance of a sensor fusion system. In this paper, the placement of cameras and radars is optimized based on the log determinant of the fused measurement noise of the sensor measurements. This is achieved by mapping the measurements into 3D Cartesian space and applying covariance intersection to obtain a final measurement distribution, which is taken as the measurement noise. The method was tested against random initial placements and optimization runs of sensors for a system that is intended for ball tracking in sports. The particular use case involves the tracking of a cricket ball for the purpose of match evaluation and assisted umpiring. However, in principle, the method is applicable to any sensor placement problem in which the objective is localization and tracking. The results indicate an improved root mean squared error for the optimized sensor placements, which in turn implies a reduction in the measurement noise covariance.