Course title
1M9876001
Probabilistic and Statistical Estimation System

 sasaki takeshi Click to show questionnaire result at 2018
Course content
In most intelligent systems including robots, the systems decide their actions based on information obtained by sensors. However, since sensor data contains noise, we need to estimate optimal values to extract necessary information. To do this, statistical and probabilistic approaches have been used. This course covers theoretical fundamentals of estimation methods based on statistical signal processing and estimation theory. Some applications such as mobile robot localization and mapping are also introduced to better understand the methods.
Purpose of class
This course introduces students to the concept of estimation based on probability theory and statistics. It also aims to help student learn how to apply each estimation method through application examples.
Goals and objectives
  1. To explain relationship among estimation methods
  2. To obtain knowledge of characteristics of each estimation method
  3. To solve problems using estimation methods
Language
Japanese(English accepted)
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1． Introduction of estimation theory Survey on estimation theory 60minutes
2． Cramer-Rao lower bound Survey on Cramer-Rao lower bound 180minutes
3． Maximum likelihood estimation Survey on maximum likelihood estimation 180minutes
4． Least squares Survey on least squares 180minutes
5． Applications (1): Curve fitting Review of maximum likelihood estimation and least squares
Survey on curve fitting 		240minutes
6． Minimum mean square error estimation Survey on minimum mean square error estimation 180minutes
7． Maximum a posteriori estimation Survey on maximum a posteriori estimation 180minutes
8． Linear minimum mean square error estimation Survey on linear minimum mean square error estimation 180minutes
9． Kalman filter (1): Dynamical signal models Survey on Gauss-Markov process 180minutes
10． Kalman filter (2): Derivation of Kalman filter Survey on Kalman filter 180minutes
11． Extended Kalman filter Survey on extended Kalman filter 180minutes
12． Applications (2): Sensor fusion in sensor networks Review on Kalman filter, survey on information filter 240minutes
13． Applications (3): Mobile robot localization - Kalman filter approach and Monte Carlo localization Review on extended Kalman filter, survey on particle filter 240minutes
14． Applications (4): SLAM (Simultaneous Localization And Mapping) Review on particle filter, survey on SLAM algorithm 250minutes
Total. - - 2650minutes
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Assignments Total.
1. 30% 30%
2. 35% 35%
3. 35% 35%
Total. 100% -
Evaluation method and criteria
Assignments (100%)
- 60% if students can solve example problems given in the lecture.
Textbooks and reference materials
Reference materials are introduced during lectures
Prerequisites
Knowledge of linear algebra, calculus, statistics, and probability theory is required
Office hours and How to contact professors for questions
  • I accept questions during the lecture or a break after the lecture in the classroom.
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
N/A
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Sun Mar 21 15:19:15 JST 2021