报告题目：Platform and Sensor Resource Management for Target Tracking
报告人: T. Kirubarajan, McMaster University, Canada
报告时间: 2018年6月23日下午 14:00-15:30
报告地点: 565net必赢最新版网页126会议室
联系人： 杨峰 13892854362
报告人简介：
Thia Kirubarajan received the B.A. and M.A. degree in electrical and information engineering from Cambridge University, England, in 1991 and 1995=3, and the M.S. and Ph.D degrees in electrical engineering from the University of Connecticut, Storrs, in 1995 and 1998,respectively. Currently, he is a professor at McMaster University.
His research interests are in estimation, target traacking, multisource information fusion, sensor resource management, signal detection, and fault diagnosis.Professor T. Kirubarajan (Kiruba) holds the title of Distinguished Engineering Professor and holds the Canada Research Chair in
Information Fusion at McMaster University, Canada. He has published about 350 research articles, 11 book chapters, one standard textbook on target tracking and four edited volumes. He is a recipient of Ontario Premiers Research Excellence Award(2002).
摘要：As a result of recent technological advances in modernized sensor sets and sensor platforms, sensor management combined with sensor platform path planning are studied to conduct intelligence, surveillance and reconnaissance (ISR) operations in novel ways. This talk addresses path planning and sensor management for a swarm of aerial vehicles to cover areas of interest (AOIs), scan objects of interest (OOIs) and/or track multiple detected targets in surveillance missions. The problems include 1) the spatio-temporal coordination of sensor platforms to observe AOIs or OOIs, 2) the optimal sensor geometry and path planning for localization and tracking of targets in a mobile three-dimensional (3D) space, and 3) the scheduling of sensors working in different (i.e., active and passive) modes combined with path planning to track targets in the presence of jammers, emerge from real-world demands and scenarios.
Platform path planning combined with sensor management is formulated as optimization problems with problem-dependent performance evaluation metrics and constraints. Firstly, to cover disjoint AOIs over an extended time horizon using multiple aerial vehicles for persistent surveillance, a joint multi-period coverage path planning and temporal scheduling, which allows revisiting in a single-period path, is formulated as a combinatorial optimization with novel objective functions. Secondly, to use a group of unmanned aerial vehicles (UAVs) cooperatively carrying out search-and-track (SAT) in a mobile 3D space with a number of targets, a joint path planning and scanning (JPPS) is formulated based on the predictive information gathered from the search space. The optimal 3D sensor geometry for target localization is also analyzed with the objective to minimize the estimation uncertainty under constraints on sensor altitude, sensor-to-sensor and sensor-to-target distances for active or passive sensors. At last, to accurately track targets in the presence of jammers broadcasting wide-band noise by taking advantage of the platform path planning and the jammer's information captured by passive sensors, a joint path planning and active-passive scheduling (JPPAPS) strategy is developed based on the predicted tracking performance at the future time steps in a 3D contested environment. The constraints on platform kinematic, flyable area and sensing capacity are included in these optimization problems.
For these multisensor path planning and decision making, solution techniques based on the genetic algorithm are developed with specific chromosome representations and custom genetic operators using either the non-dominated sorting multiobjective optimization (MOO) architecture or the weighted-sum MOO architecture. Simulation results illustrate the performance and advantage of the proposed strategies and methods in real-world surveillance scenarios.
