《信息融合》课程简介

Introduction of Information Fusion

 

课程名称 (Course Name) ：信息融合  Information Fusion

课程代码 (Course Code）：C032712

学分/学时 (Credits/Credit Hours)：2.0 /36

开课时间 (Course Term )：春 spring

开课学院（Course School）: 电子信息与电气工程学院  seiee

任课教师（Teacher）: Anders Lindquist

课程讨论时数（Course Hours）: Thursdays and Fridays 13:00—14:40 小时(Hours)

课程实验数（Lab Hours）: (Hours)

 

课程内容简介(Course Contents Introduction):

An introduction to a number of important topics in sensor and data fusion.

 

教学大纲(Course Outline):

Week 1:      Linear static models 

Week 2:      Nonlinear static models

Week 3:      Sensor networks, detection and classification

Week 4:     Filter theory, Kalman filtering

Week 5:      Kalman filtering, continued, moment problems

Week 6:      Extended Kalman filtering and Kalman filtering banks

Week 7:     Particle filtering

Week 8:      Applications

 

课程进度计划(Course Schedule):

Week 1（2月28日、3月1日）: Linear static models

Week 2（3月7日、3月8日）:   Nonlinear static models

Week 3（3月14日、3月15日）:     Sensor networks, detection and classification

Week 4（3月21日、3月22日）:     Filter theory, Kalman filtering

Week 5（3月28日、3月29日）:     Kalman filtering, continued, moment problems

Week 6（4月6日）:      Extended Kalman filtering and Kalman filtering banks

Week 7（4月11日、4月12日）:     Particle filtering

Week 8（4月18日、4月19日）:     Applications

 

课程考核要求(Course Examination Requirements)

A term paper on how to use course material in the student’s own area of interest or research

 

参考文献(Course References)

Fredrik Gustafsson, Statisitical Sensor Fusion, Studentlitteratur, Lund, Sweden

Anders Lindquist, Course Notes in Information Fusion