The main characteristic of the particle swarm optimization（PSO） algorithm is that it can get the solution quickly

and its disadvantage is easy to fall into a local optimum in the process.An improved PSO algorithm is proposed using best dimension mutation technique and quantum theory.It is applied to the sensor scheduling problem for target tracking.The dynamics model of the target is linear Gaussian model

and the sensor measurements impaired by white Gaussian noise are linearly related to the state of the target.A numerical problem of single target tracking with multiple sensors is studied using the proposed best dimension mutation particle swarm optimization（BDMPSO） algorithm to get the minimal cost on the whole timeline.The simulation results show that the proposed algorithm has much faster convergence

stronger global search ability and more scheduling efficient than the existing algorithms.