SF Course
This is work in progress for developing a series of modules for self studies in sensor fusion. Each module consists of a video lecture, slides, reading advice and recommended exercises. The videos are also available as a YouTube playlist for your convenience, sorted in the way we anticipate make them the most accessible.
| Nr | Subject | Book Chapter | Material |
| 1 | Introduction | 1 | slides, video (7:50) |
| 2 | WLS | 2-2.2 | slides, video (9:15) |
| 3 | The Fusion Formula | 2.3 | slides, video (6:41) |
| 4 | Safe Fusion | 2.3.5 | slides, video (6:56) |
| 5 | ML and CRLB | 2.4-2.5 | slides, video (9:34) |
| 6 | 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), m-files (for 7-9) |
| 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) |
| 10 | Sensor networks: NLS | 4-4.2 | slides, video (12:59) |
| 11 | Sensor networks: Tricks | 4.3-4.6 | slides, video (11:14) |
| 12 | Detection theory | 5 | slides, video (11:21) |
| 13 | Bayes versus Fisher | 6 Intro | slides, video (9:43) |
| 14 | Bayes Filtering Recursion | 6.3 | slides, video (10:18) |
| 15 | Filtering CRLB | 6.5 | slides, video (9:59) |
| 16 | Continuous Time Motion Models | 13-13.1,13.4 | slides, video (8:09) |
| 17 | Discretizing Motion Models | 12 | slides, video (11:50) |
| 18 | Rotational Kinematics | 13.2-13.3 | slides, video (14:55) |
| 19 | Wheel Speed Sensor Application | 14.3 | slides, video (6:48) |
| 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) |
| 25 | Application: shooter localization | 16.1 | slides, video (8:58) |
| 26 | Application: Kalman filters | 16.2 | slides, video (18:09) |
| 27 | Point Mass Filter | 9.1-9.2 | slides, video (14:39) |
| 28 | Particle Filter | 9.3 | slides, video (17:23) |
| 29 | Particle Filter Properties | 9.4-9.6 | slides, video (16:15) |
| 30 | Marginalized Particle Filter | 9.8 | slides, video (17:58) |
| 31 | Application: Particle filters | 16.3 | slides, video (19:16) |
| 32 | Filter Banks | 10 | slides, video (21:10) |
| 33 | Application: Kalman filter banks | 14.2.4 | slides, video (9:01) |
| 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) | |
| 38 | Lab Work: Localization Using a Microphone Network | Introduction to Lab 1 | slides, video (10:23) |
| 39 | Lab Work: Orientation Estimation using Smartphone Sensors | Introduction to Lab 2 | slides, video (14:40) |
| 40 | Signal and Systems Matlab Toolbox | Introduction to the SigSys | slides, video (21:36) |