Computational Complexity Comparison Of Multi-Sensor Single Target Data Fusion Methods By Matlab

摘要

Target tracking using observations from multiple sensors can achieve betterestimation performance than a single sensor. The most famous estimation tool intarget tracking is Kalman filter. There are several mathematical approaches tocombine the observations of multiple sensors by use of Kalman filter. Animportant issue in applying a proper approach is computational complexity. Inthis paper, four data fusion algorithms based on Kalman filter are consideredincluding three centralized and one decentralized methods. Using MATLAB,computational loads of these methods are compared while number of sensorsincreases. The results show that inverse covariance method has the bestcomputational performance if the number of sensors is above 20. For a smallernumber of sensors, other methods, especially group sensors, are moreappropriate..