讲学题目: Heterogeneous Track-to-Track Fusion in 2D and 3D
报告内容简介：Homogeneous track-to-track fusion (T2TF) in a multisensor tracking system has been widely studied. However, research on heterogeneous T2TF is limited at present. A common limitation of the current work on heterogeneous T2TF is that the cross covariance due to common process noise cannot be computed. This is because two local trackers use different dynamic models, and hence, the common process noise does not exist. In our recent works, we considered the heterogeneous T2TF problem in 2D and 3D. In this talk we shall first review the existing research on heterogeneous T2TF. Then we shall present our work in 2D and 3D, which overcomes existing limitations. This talk will focus primarily on the 3D heterogeneous T2TF problem. For the 3D problem, we used a passive infrared search and track (IRST) sensor and an active air moving target indicator (AMTI) radar with the nearly constant velocity motion of the target. The active AMTI tracker uses the Cartesian state vector with 3D position and velocity, and the dynamic model is linear. A passive IRST tracker commonly uses modified spherical coordinates (MSC) for the state vector, where the dynamic model is nonlinear. In this formulation, the common process noise is explicitly modeled in both dynamic models. Therefore, it is possible to take into account the common process noise. We use the cubature Kalman filter (CKF) in both trackers due to its numerical stability and improved state estimation accuracy over existing nonlinear filters. The passive tracker used a range-parameterized MSC-based CKF, and the active tracker uses a Cartesian CKF. We performed T2TF using the information filter (IF), where each local tracker sends its information matrix and the corresponding information state estimate to the fusion center. The IF handles the common process noise in an approximate way. Results from Monte Carlo simulations show that the accuracy of the proposed IF-based T2TF is close to that of the centralized fusion with varying levels of process noise and communication data rate.
讲学题目: Technical Writing
Dr. Mallick received his Ph.D. degree in quantum solid-state theory from the State University of New York, Albany, NY, USA, in 1981 and an M.S. degree in computer science from Johns Hopkins University, Baltimore, MD, USA, in 1987. He is currently an independent consultant in Anacortes, WA, USA. He has worked on satellite orbit and attitude determination in NASA programs. He is a co-editor and a co-author of the book entitled Integrated Tracking, Classification, and Sensor Management: Theory and Applications (New York, NY, USA: Wiley/IEEE, 2012). His research interests include nonlinear filtering, out-of-sequence measurement (OOSM) algorithms, and measures of nonlinearity, GMTI filtering and tracking, multisensor multitarget tracking, multiple hypothesis tracking, random-finite-set-based multitarget tracking, space object tracking, distributed fusion, and heterogeneous track-to-track fusion. Dr. Mallick was the Associate Editor-in-Chief of the online journal of the International Society of Information Fusion (ISIF) in 2008–2009. He was a member of the Board of Directors of the ISIF in 2008–2010. He was the Lead Guest Editor of the special issue on “Multitarget Tracking” in the IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING in June 2013. He is currently an Associate Editor for target tracking and multisensor systems of the IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS.