Due to the limited sensing range for sensors

moving target tracking has to be realized by relaying from one sensor to the other in sensor networks.Thus

the tracking procedure can be modeled as a Markovian chain system.By reconstructing the innovation equation

the relaying Kalman filter(RKF) algorithm is designed in the light of the Bayesian theory.On this basis

the interacting multiple sensor filter(IMSF) algorithm is proposed further by mixing the initial state and covariance at one cycle

which has a bit better tracking performance than the RKF algorithm

but at the cost of the computational complexity.Finally

simulation results show the effectiveness of the proposed algorithms.