In decentralized data fusion system, if the probability model of the noise is Gaussian and the innovation informations from the sensors are uncorrlated,the information filtering technique can be the best method to fuse the information from different sensors. However, in the realistic environments, information filter cannot provide the best solution of state estimation and data integration when the noises are non-Gaussian and correlated. Since particle filter are capable of dealing with non-linear and non-Gaussian problems, it is an intuitive approach to replace the information filter by particle filter with some suitable data fusion techniques.In this thesis, we investigate a decentralized data fusion system with improved particle filters for target tracking. In order to achieve better tracking performance, the Iterated Extended Kalman Filter framework is used to incorporate the newest observations into the proposal distribution of the particle filter. In our proposed architecture, each sensor consists of one particle filter, which is used in generating the local statistics of the system state. Gaussian mixture model (GMM) is adopted to approximate the posterior distribution of the weighted particles in the filters, thereby more compact representations of the distribution for transmmision can be obtained. To achieve information sharing and integration, the GMM-Covariance Intersection algorithm is used in formulating the decentralized fusion solutions. Simulation resluts of target tracking cases in a sensor system with two sensor nodes are given to show the effectiveness and superiorty of the proposed architecture.

In this thesis, we investigate a decentralized data fusion system with improved particle filters for target tracking. In many application areas, it becomes essential to use nonlinear and non-Gaussian elements to accurately model the underlying dynamics of a physical system. Particle filters have a great potential for solving highly nonlinear and non-Gaussian estimation problems, in which the traditional Kalman filter and extended Kalman filter may generally fail. To improve the tracking performance of particle filters, initialization of the particles is studied. We construct an initial state distribution by using least square estimation. In addition, to enhance the tracking capability of particle filters, representation of target velocity by another set of particles is considered. We include another layer of particle filter inside the original particle filter for updating the velocity. In our proposed architecture, we assume that each sensor node contain a particle filter and there is no fusion center in the sensor network. Approximated a posteriori distribution at the sensor is obtained by using the local particle filters with the Gaussian mixture model (GMM), so that more compact representations of the distribution for transmission can be obtained. To achieve information sharing and integration, the GMM-covariance intersection algorithm is used in formulating the decentralized fusion solutions. Simulation results are presented to illustrate that the performance of the improved particle filter is better than standard particle filter. In addition, simulation results of target tracking in the sensor system with three sensor nodes are given to show the effectiveness and superiority of the proposed architecture.