Sensor Fusion (TSRT14)
On-Line Material
In 2020 and 2021 this course was, due to the COVID-19 pandemic, given
as an online course. The video material that was developed is still
available, as a complement to the lecture series.
Videos
The material should be considered as work in progress, and covers the
course material using a number of modules. Each module comprising a
video, matching slides, and recommended reading. The modules are
listed below. The modules are followed by a table relating the
modules to the lectures in the course.
The videos are also available as a
Youtube playlist
for your convenience, sorted in the way we anticipate make them the
most accessible.
Lecture 1. Estimation theory for linear models
Lecture 2. Estimation theory for nonlinear models
| Video |
Topic |
Book Chapter |
Material |
| 6 |
Nonlinear Least Squares (NLS) |
3-3.2, 3.6 |
slides,
video
(12:23)
|
| 7 |
Parameter Estimation using Nonlinear Transformations |
3.4 (first part), 3.5 |
slides,
video
(12:26)
|
| 8 |
Nonlinear Transformations Using Taylor Series Expansions |
3.4.3 |
slides,
video
(6:53)
|
| 9 |
Nonlinear Transformations Using Samples |
3.4.2, 3.4.4 |
slides,
video
(9:49)
|
Lecture 3. Cramér-Rao lower bound (CRLB). Models for sensor network applications
| Video |
Topic |
Book Chapter |
Material |
| 10 |
Sensor networks: NLS |
4-4.2 |
slides,
video
(12:59)
|
| 11 |
Sensor networks: Tricks |
4.3-4.6 |
slides,
video
(11:14)
|
Lecture 4. Detection theory. Filter theory.
| Video |
Topic |
Book Chapter |
Material |
| 12 |
Detection |
5 |
slides,
video
(11:21)
|
| 13 |
Bayes versus Fisher |
6 Intro |
slides,
video
(9:43)
|
| 14 |
Bayes Optimal Filter Recursion |
6.3 |
slides,
video
(10:18)
|
| 15 |
Filtering CRLB |
6.5 |
slides,
video
(9:59)
|
| 25 |
Application: shooter localization |
16.1 |
slides,
video
(8:58)
|
Lecture 5. Modeling and motion models
| Video |
Topic |
Book Chapter |
Material |
| 16 |
Continuous Time Motion Models |
13-13.1,13.4 |
slides,
video
(8:09)
|
| 17 |
Discretizing Motion Models |
12 |
slides,
video
(11:50)
|
| 18 |
Kinematic Models |
13.2-13.3 |
slides,
video
(14:55)
|
| 19 |
Odometric Motion Model (Lemma 7,1) |
14.3 |
slides,
video
(6:48)
|
Lecture 6. Kalman filter. Kalman filter approximations for nonlinear models (EKF, UKF)
| Video |
Topic |
Book Chapter |
Material |
| 20 |
Conditional Gaussian Distribution |
7.1.3 |
slides,
video
(7:06)
|
| 21 |
Kalman Filter |
7-7.1 |
slides,
video
(15:39)
|
| 22 |
Kalman Filter Properties |
7.2-7.7 |
slides,
video
(9:25)
|
| 23 |
Extended Kalman Filter (EKF) |
8 (EKF related parts) |
slides,
video
(12:47)
|
| 24 |
Unscented Kalman Filter (UKF) |
8 (UKF related parts) |
slides,
video
(11:58)
|
| 26 |
Application: Kalman filters |
16.2 |
slides,
video
(18:09)
|
Lecture 7. The point-mass filter and the particle filter
| Video |
Topic |
Book Chapter |
Material |
| 27 |
Point Mass Filter |
9.1-9.2 |
slides,
video
(14:39)
|
| 28 |
Particle Filter |
9.3 |
slides,
video
(17:23)
|
Lecture 8. The particle filter theory. The marginalized particle filter. Filter banks
| Video |
Topic |
Book Chapter |
Material |
| 29 |
Particle Filter Properties |
9.4-9.6 |
slides,
video
(16:15)
|
| 30 |
Marginalized Particle Filter |
9.8 |
slides,
video
(17:58)
|
| 31 |
Particle Filter Applications |
16.3 |
slides,
video
(19:16)
|
| 32 |
Filter Banks |
10 |
slides,
video
(21:10)
|
| 33 |
Kalman Filter Banks Applications |
14.2.4 |
slides,
video
(9:01)
|
Lecture 9. Simultaneous localization and mapping (SLAM)
| Video |
Topic |
Book Chapter |
Material |
| 34 |
Simultaneous Localization and Mapping (SLAM): problem formulation |
11-11.1 |
slides,
video
(13:25)
|
| 35 |
Simultaneous Localization and Mapping (SLAM): EKF SLAM |
11.2 |
slides,
video
(15:38)
|
| 36 |
Simultaneous Localization and Mapping (SLAM): FastSLAM |
11.3 |
slides,
video
(15:13)
|
| 37 |
Application: RSS-SLAM |
|
slides,
video
(14:30)
|
Lecture 10. Sensors and sensor-near signal processing. Filter and model validation
Miscellaneous
| Video |
Topic |
Book Chapter |
Material |
| 38 |
Lab Work: Localization Using a Microphone Network |
Introduction Lab 1 |
slides,
video
(10:23)
|
| 39 |
Lab Work: Orientation Estimation using Smartphone Sensors |
Introduction Lab 2 |
slides,
video
(14:40)
|
| 40 |
Signal and Systems Matlab Toolbox |
Introduction SigSys |
slides,
video
(21:36)
|