Data fusion algorithm for uncertain measurement in sensor networks

In sensor network, several kinds of information with different characteristics are usually sensed by the sensor nodes, and measurement fusion is performed on sink nodes. When availability of measurement is uncertain, itpsilas difficult to construct uniform global observation vector and observation matrix appropriately. To resolve the problem a measurement fusion algorithm for uncertain measurement is presented. By defining availability function for each dimension of system observation vector to construct generalized observation vector and covariance matrix, uncertainty of measurement is expressed, and current measurement fusion algorithm is generalized to uncertain scenario, and optimal fusion result is obtained using Kalman filtering. To be convenient for numerical calculation, suboptimal measurement fusion algorithm is put forward also. Simulation results show that this method can deal with the multisensor measurement fusion of uncertain availability correctly, and calculational cost is almost as same as one of current algorithm.