No description available.

Bibliography: leaves 149-152.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 1997. / Thesis Advisor(s): Murali Tummala. "September 1997." Includes bibliographical references (p. 61-62). Also available in print.

Thesis (Ph. D.)--Drexel University, 2005. / Includes abstract and vita. Includes bibliographical references (leaves 109-114).

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 solving target tracking problems, the Kalman filter (KF) is one of the most widely used estimators. Whether the state of target movement adapts to the changes in the observations depends on the model assumptions. The interacting multiple model (IMM) algorithm uses interaction of a bank of parallel Kalman filters to solve the hypothetical model of tracking maneuvering target. Based on the function of soft switching, the IMM algorithm, with parallel Kalman filters of different dimensions, can perform well by adjusting the model weights. Nonetheless, the uncertainty in measured data and the types of sensing systems used for target tracking may still hinder the signal processing in the IMM. In order to improve the performance of target tracking and signal estimation, the concept of data fusion can be adapted in the IMM-based structures. Multiple IMM based estimators can be used in the structure of multi-sensor data fusion. In this thesis, we propose a dual-IMM estimator structure, in which data fusion of the two IMM estimators is achieved by updating associated model probabilities. Suppose that two sensors for measuring the moving target is affected by the different degrees of noise, the measured data can be processed first through two separate IMM estimators. Then, the IMM-based estimators exchange with each other the estimates, model probabilities and model transition probabilities. The dual-IMM estimator will integrate the shared data based on the proposed dual-IMM algorithm. The dual-IMM estimator can be used to avoid degraded performance of single IMM with insufficient data or undesirable environmental effects. The simulation results show that a single IMM estimator with smaller measurement noise level can be used to compensate the other IMM, which is affected by larger measurement noise. Improved overall performance from the dual-IMM estimator is obtained. Generally speaking, the two IMM estimators in the proposed structure achieve better performance when same level of measurement noise is assumed. The proposed dual-IMM estimator structure can be easily extended to multiple-IMM structure for estimation and data fusion.

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.

Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Eugene P. Paulo, Sergio Posadas, Susan M. Sanchez. Includes bibliographical references (p. 63-64). Also available online.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

In this thesis, seven multi-sensor data fusion based estimation techniques are investigated. All methods are compared in terms of their ability to fuse data from loop detectors and Bluetooth tracked probe vehicles to accurately estimate freeway traffic speed. In the first case study, data generated from a microsimulation model are used to assess how data fusion might perform with present day conditions, having few probe vehicles, and what sort of improvement might result from an increased proportion of vehicles carrying Bluetooth-enabled devices in the future. In the second case study, data collected from the real-world Bluetooth traffic monitoring system are fused with corresponding loop detector data and the results are compared against GPS collected probe vehicle data, demonstrating the feasibility of implementing data fusion for real-time traffic monitoring today. This research constitutes the most comprehensive evaluation of data fusion techniques for traffic speed estimation known to the author.