List of Accepted Tutorials

    1. Dr. Mahendra Mallick
    PRESENTER BIOGRAPHY:
    Dr. Mahendra Mallick is an independent consultant. He received a Ph.D. degree in Quantum Solid State Theory from the State University of New York at Albany and an MS degree in Computer Science from the Johns Hopkins University. He is a senior member of the IEEE and was the Associate Editor-in-chief of the online journal of the International Society of Information Fusion (ISIF) during 2008-2009. He was member of the board of directors of the ISIF during 2008-2010. He is a co-editor and an author of the book, Integrated Tracking, Classification, and Sensor Management: Theory and Applications, Wiley-IEEE, December 2012. He was the Lead Guest Editor of the Special Issue on Multitarget Tracking in the IEEE Journal of Selected Topics in Signal Processing, June, 2013. His current research includes multisensor multitarget tracking, multiple hypothesis tracking, random finite set based multitarget filtering, space object tracking, distributed fusion, and nonlinear filtering.

    Tutorial Title: Space Object Tracking

    Tutorial Abstract
    Overview of SO tracking: Current status of space objects and implications on future space program. Types of orbits – LEO, mid-Earth orbit, geosynchronous orbit, and highly elliptical orbit. Precision orbit determination (OD) and OD. Mathematical preliminary, coordinate frames and systems, and time systems. Introduction to orbit determination: Translational and rotational equations of motion for a SO. Force models – gravity, atmospheric drag force, solar radiation pressure, and thrust. Sensors and measurement models: sensor network for SO tracking, radar and optical sensor, light-time corrections, aberration, and atmospheric refraction correction. Two-body problem: Kepler’s laws, orbital elements. Initial OD from range and angle measurements, OD from angle-only measurements, Gibbs algorithm, Lambert-Euler method, and Gauss method. Review of nonlinear filtering algorithms for OD: Continuous-discrete filtering, weighted least squares or differential correction, extended Kalman filter, unscented Kalman filter, Gaussian sum filter. Review of candidate algorithms for multitarget tracking: Centralized and distributed tracking, multiple hypothesis tracking, random finite set based multitarget filtering algorithms, cardinalized probability hypothesis density and multi-Bernoulli filters.

    2. Dr. Reza Hoseinnezhad, Royal Melbourne Institute of Technology University, Australian.
    PRESENTER BIOGRAPHY:
    Dr. Hoseinnezhad obtained a PhD in Electrical Engineering from The University of Tehran in 2002. He is mainly involved in teaching, research and administrative duties. He has published over 50 papers in refereed conferences and journals. He is an Oz-reader for the Engineering and Environmental Sciences panel of the Australian Research Council (ARC). In this role, he has assessed several ARC Linkage Project, Discovery Project and Centre of Excellence applications for the ARC. He is a reviewer of several international journal publications such as IEEE Transactions on Signal Processing, IEEE Transaction on Vehicular Technology, IEEE Transactions on Industrial Electronics, and Pattern Recognition.

    Tutorial Title: Recent Developments in Multi-Bernoulli Solutions for Multi Target Tracking Problems

    Tutorial Abstract
    In many computer science and engineering applications, the states of multiple objects need to be estimated. The problem is especially challenging when in addition to the states, the number of objects are unknown and randomly vary with time. These multi-target tracking problems (also called “multi-object estimation” problems) arise in a host of applications areas including aerospace, defense, field robotics, communications, environmental, and biomedical research, and are becoming more important with the proliferation of sensing technologies. Multi-object filtering generalizes classical paradigms such as Bayesian/Kalman filtering to multi-object systems, and is a challenging problem both in theory and practice. The last decade has witnessed exciting developments with the introduction of random finite set theory to multi-object filtering. The random finite set framework has led to the development of the multi-Bernoulli and labeled multi-Bernoulli filters. This workshop presents a general introduction to multi-object filtering, recent multi-Bernoulli and labeled multi-Bernoulli solutions and advances in filtering from video data. This is a fertile area of research and many aspects of its applications have not been explored yet. The workshop is generally targeted at postgraduate students and researchers as well as all those who have an interest in target tracking. It is expected that the material presented in the workshop provides the students and researchers with fresh ideas to apply the new approaches for developing efficient solutions for their research problems.