Description: The rapid development of advanced sensors and their joint application provide a foundation for new paradigms to combat the challenges that arise in target detection, tracking and forecasting in harsh environments with poor prior information. As a consequence, the sensor community has expressed interest in novel data mining methods coupling traditional statistical techniques for substantial performance enhancement. For example, the advent of multiple/massive sensor systems provides very rich observation at high frequency yet low financial cost, which facilitates novel perspectives based on data clustering and model learning to deal with false alarms and misdetection, given little statistical knowledge about the objects, sensors and the background. Numerical fitting and regression analysis provide another unlimited means to utilize the unstructured context information such as “the trajectory is smooth” for continuous-time target trajectory estimation.
Incorporating additional, readily available information to constrain the adaptive response and to combat poor scenario knowledge, has shown promise as a means of restoring sensor capability over a range of challenging operating conditions as well as to deal with a variety of challenging problems that makes traditional approaches awkward. The purpose of this special section is to assemble and disseminate information on recent, novel advances in sensor signal and data mining techniques and approaches, and promote a forum for continued discussion on the future development. Both theoretical and practical approaches in the area are welcomed.

Organizers: Tiancheng Li (t.c.li@usal.es ) Haibin Ling (hbling@temple.edu) and Genshe Chen (gchen@intfusiontech.com)