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Utilization of information acquired from a sensor network to improve the tracking accuracy is one of the most important issues in sensor network research. An approach that consists of sensor-based filtering algorithms, local processors and global processor is employed to describe the distributed fusion problem when several sensors execute surveillance over the certain area. For sensor tracking systems, each filtering algorithm utilized in the Reference Cartesian Coordinate System (RCCS) is presented for target tracking when the radar measures range, bearing and elevation angle in the Spherical Coordinate System (SCS). For local processors, each track-to-track fusion algorithm is used to merge two tracks representing the same target. The number of 2-combinations of a set with N distinct sensors is considered for central track fusion. For global processor, a Bayesian method with SemiMarkov process is employed to develop the Modified Probabilistic Neural Network (MPNN) algorithm to compute the weights of local processors. By using local processor weights, the subsequent data fusion is executed to combine the corresponding joint state estimates. Simulation results are presented comparing the performance of the global MPNN algorithm with the local-level fusion algorithm and with the sensor-level filtering algorithm.

An integrated approach that consists of sensor-based filtering algorithms, local processors, and a global processor is employed to describe the distributed fusion problem when several sensors execute surveillance over a certain area. For the sensor tracking systems, each filtering algorithm utilized in the reference Cartesian coordinate system is presented for target tracking, with the radar measuring range, bearing, and elevation angle in the spherical coordinate system (SCS). For the local processors, each track-to-track fusion algorithm is used to merge two tracks representing the same target. The number of 2combinations of a set with N distinct sensors is considered for central track fusion. For the global processor, the data fusion algorithms, simplified maximum likelihood (SML) estimator and covariance matching method (CMM), based on linear minimum variance (LMV) estimation fusion theory, are developed for use in a centralized track-to-track fusion situation. The resulting global fusers can be implemented in a parallel structure to facilitate estimation fusion calculation. Simulation results show that the proposed SML estimator has a more robust capability of improving tracking accuracy than the CMM and the LMV estimators.

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