  Target tracking using observations from multiple sensors can achieve better
estimation performance than a single sensor. The most famous estimation tool in
target tracking is Kalman filter. There are several mathematical approaches to
combine the observations of multiple sensors by use of Kalman filter. An
important issue in applying a proper approach is computational complexity. In
this paper, four data fusion algorithms based on Kalman filter are considered
including three centralized and one decentralized methods. Using MATLAB,
computational loads of these methods are compared while number of sensors
increases. The results show that inverse covariance method has the best
computational performance if the number of sensors is above 20. For a smaller
number of sensors, other methods, especially group sensors, are more
appropriate..
