Course Name Code Semester T+U Hours Credit ECTS
Kalman Filtering BSM 604 0 3 + 0 3 6
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Doctorate Degree
Course Type Optional
Course Coordinator Prof.Dr. AHMET ÖZMEN
Course Lecturers
Course Assistants
Course Category
Course Objective Kalman filters are tools used on many enginnering areas such as communication, signal processing problems, space problems and defense systems etc. Therefore it is valuable for computer engineeringdepartment.
Course Content Introduction to Kalman filters, dynamic systems, linear systems and their solutions, discrete linear systems and their solutions. Observability of linear dynamic systems. Applications of kalman filters.
# Course Learning Outcomes Teaching Methods Assessment Methods
1 Learn Kalman Filtering Lecture, Question-Answer, Drilland Practice, Self Study, Testing, Homework,
2 Learn applicatio of kalman filters in engineering Lecture, Question-Answer, Drilland Practice, Testing, Homework,
Week Course Topics Preliminary Preparation
1 Introduction to Kalman Filtering, Estimation Methods
2 Dynamic Systems, Continuous Lineer Systems and Their Solutions
3 Discrete Lineer Systems and their solutions, Observability of Lineer Dynamic System Models, Procedures for Computing Matrix Exponential
4 Discovery and Modeling of Random Processes Probability and Random Variables
5 Statistical Properties of Random Variables, and Random Process, Lineer Models of Random Processes and Sequences
6 Shaping Filters and State Augmentation, Covariance Propagation Equations Orthogonality Princible
7 Estimation Problem, Kalman Filter
8 Kalman_Bucy Filter, Optimal Linear Predictors Correlated Noise Sources, Relationships between Kalman and Wiener Filters, Quadratic Loss Functions
9 Matrix Riccati Differential Equations, Matrix Riccati Equation in Discrete Time
10 Relationships between Continuous and Discrete Riccati Equations, Model Equations for Transformed State Variables, Applications of Kalman Filters, Smoothers
11 Nonlinear Estimation Problems, Problem Statement Linearization Methods, Linearization about a Nominal Trajectory Linearization about the Estimated Trajectory,Discrete Linearized and Extended Filtering
12 Discrete extended Kalman Filter, Continuous Linearized and Extended Filters, Biased Errors in Quadratic Measurements, Application of Nonlinear Filters
13 Implementation Methods ,Effect of Roundoff Errors on Kalman Filters
14 Earlier Implemantation Methods, Factorization Methods for Kalman Filtering
Resources
Course Notes
Course Resources Kalman Filtering [Theory and Practice]/ Mohinder S.Grawal, Angus P. Andrews, 1993
Probability and Stochastic Processes for Enginers/ Carl W.Helstrom,1984
Introduction to Stochastic Processes/ Paul G.Hoel, Sidney C.Port, Charles J.Stone 1972
Probability, Random Variables and Stochastics Processes/ Athanasios Papoulis , 1991
Lessons in Estimation Theory for Signal Processing , Communications and Control / Jerry M.Mendel, 1995
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 ability to access wide and deep information with scientific researches in the field of Engineering, evaluate, interpret and implement the knowledge gained in his/her field of study X
2 ability to complete and implement “limited or incomplete data” by using the scientific methods. X
3 ability to consolidate engineering problems, develop proper method(s) to solve and apply the innovative solutions to them X
4 ability to develop new and original ideas and method(s), to develop new innovative solutions at design of system, component or process X
5 gain comprehensive information on modern techniques, methods and their borders which are being applied to engineering X
6 ability to design and apply analytical, modelling and experimental based research, analyze and interpret the faced complex issues during the design and apply process X
7 gain high level ability to define the required information and data X
8 ability to work in multi-disciplinary teams and to take responsibility to define approaches for complex situations X
9 systematic and clear verbal or written transfer of the process and results of studies at national and international environments X
10 aware of social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation and announcement X
11 aware of new and developing application of profession and ability to analyze and study on those applications X
12 ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment X
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 100
Total 100
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 20 20
Final examination 1 25 25
Total Workload 141
Total Workload / 25 (Hours) 5.64
dersAKTSKredisi 6