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Linear Kalman Filter Deep Dive (and Target Tracking)

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Linear Kalman Filter Deep Dive (and Target Tracking)

Dieser Kurs ist Teil von Spezialisierung „Applied Kalman Filtering“

Dozent: Gregory Plett

Bei enthalten

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4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

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2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Kompetenzen, die Sie erwerben

Kategorie: Linear Algebra
Kategorie: Time Series Analysis and Forecasting
Kategorie: Estimation
Kategorie: Probability Distribution
Kategorie: Predictive Analytics
Kategorie: Applied Mathematics
Kategorie: Statistical Inference
Kategorie: Correlation Analysis
Kategorie: Probability & Statistics
Kategorie: Numerical Analysis
Kategorie: Mathematical Modeling
Kategorie: Markov Model

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Kategorie: Matlab

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung „Applied Kalman Filtering“

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In diesem Kurs gibt es 4 Module

As a follow-on course to "Kalman Filter Boot Camp", this course derives the steps of the linear Kalman filter to give understanding regarding how to adjust the method to applications that violate the standard assumptions. Applies this understanding to enhancing the robustness of the filter and to extend to applications including prediction and smoothing. Shows how to implement a target-tracking application in Octave code using an interacting multiple-model Kalman filter.

Moduldetails

Knowing how to derive the steps of the Kalman filter is important for understanding the assumptions that are made and to be able to re-derive the steps for different assumptions. This week, you will learn how to derive the steps and will gain insight into how the Kalman filter works.

Das ist alles enthalten

7 Videos12 Lektüren6 Aufgaben1 Diskussionsthema

7 VideosInsgesamt 116 Minuten

2.1.1: Welcome to the course!8 Minuten
2.1.2: Predict/correct mechanism of sequential probabilistic inference27 Minuten
2.1.3: The Kalman-filter gain factor25 Minuten
2.1.4: Summarizing the six steps of generic sequential probabilistic inference8 Minuten
2.1.5: Deriving the three linear Kalman-filter prediction steps21 Minuten
2.1.6: Deriving the three linear Kalman-filter correction steps24 Minuten
2.1.7: Summary of "Deriving the linear Kalman filter" module plus next steps4 Minuten

12 LektürenInsgesamt 120 Minuten

Frequently Asked Questions10 Minuten
Course Resources10 Minuten
How to Use Discussion Forums10 Minuten
Earn a Course Certificate10 Minuten
Are you interested in earning an online MSEE degree?10 Minuten
Notes for Lesson 2.1.110 Minuten
Notes for Lesson 2.1.210 Minuten
Notes for Lesson 2.1.310 Minuten
Notes for Lesson 2.1.410 Minuten
Notes for Lesson 2.1.510 Minuten
Notes for Lesson 2.1.610 Minuten
Notes for Lesson 2.1.710 Minuten

6 AufgabenInsgesamt 80 Minuten

Graded assignment for week 130 Minuten
Practice assignment for Lesson 2.1.210 Minuten
Practice assignment for Lesson 2.1.310 Minuten
Practice quiz for Lesson 2.1.410 Minuten
Practice quiz for Lesson 2.1.510 Minuten
Practice assignment for Lesson 2.1.610 Minuten

1 DiskussionsthemaInsgesamt 10 Minuten

Introduce yourself10 Minuten

Last week, you learned the assumptions made when deriving the Kalman filter. What if these assumptions are not met correctly? What if numeric roundoff error causes failure? This week, you will learn how to solve problems with the standard Kalman filter.

Das ist alles enthalten

7 Videos7 Lektüren7 Aufgaben3 Unbewertete Labore

7 VideosInsgesamt 121 Minuten

2.2.1: How do we improve the numeric robustness of a Kalman filter?15 Minuten
2.2.2: How do we increase the precision of the linear Kalman filter?28 Minuten
2.2.3: How do I initialize and tune a Kalman filter?21 Minuten
2.2.4: What do we do when the noises are nonzero-mean?19 Minuten
2.2.5: What do I do if the process and sensor noises are cross-correlated?19 Minuten
2.2.6: What about when the process and sensor noises are not white?16 Minuten
2.2.7: Summary of "Making the linear Kalman filter bulletproof" module plus next steps3 Minuten

7 LektürenInsgesamt 70 Minuten

Notes for Lesson 2.2.110 Minuten
Notes for Lesson 2.2.210 Minuten
Notes for Lesson 2.2.310 Minuten
Notes for Lesson 2.2.410 Minuten
Notes for Lesson 2.2.510 Minuten
Notes for Lesson 2.2.610 Minuten
Notes for Lesson 2.2.710 Minuten

7 AufgabenInsgesamt 90 Minuten

Graded assignment for week 230 Minuten
Practice assignment for Lesson 2.2.110 Minuten
Practice assignment for Lesson 2.2.210 Minuten
Practice assignment for Lesson 2.2.310 Minuten
Practice assignment for Lesson 2.2.410 Minuten
Practice assignment for Lesson 2.2.510 Minuten
Practice assignment for Lesson 2.2.610 Minuten

3 Unbewertete LaboreInsgesamt 60 Minuten

Lab to compare standard and square-root Kalman filters20 Minuten
Lab to compare KF with and without bias correction20 Minuten
Lab to compare KF with and without compensation for autocorrelated noises20 Minuten

The standard linear Kalman filter works well for state estimation, but can be extended to implement prediction and smoothing as well. Further, we can speed up the steps or even eliminate steps in some circumstances. This week, you will learn some extensions and refinements to linear Kalman filters.

Das ist alles enthalten

7 Videos7 Lektüren7 Aufgaben3 Unbewertete Labore

7 VideosInsgesamt 130 Minuten

2.3.1: Automatically detecting bad measurements with a Kalman filter21 Minuten
2.3.2: Processing measurements sequentially for multi-output systems21 Minuten
2.3.3: Using the Kalman filter for prediction19 Minuten
2.3.4: Using the Kalman filter for smoothing18 Minuten
2.3.5: Steady-state Kalman filters19 Minuten
2.3.6: Continuous-time Kalman filters30 Minuten
2.3.7: Summary of "Extensions and refinements to linear Kalman filters" module plus next steps2 Minuten

7 LektürenInsgesamt 70 Minuten

Notes for Lesson 2.3.110 Minuten
Notes for Lesson 2.3.210 Minuten
Notes for Lesson 2.3.310 Minuten
Notes for Lesson 2.3.410 Minuten
Notes for Lesson 2.3.510 Minuten
Notes for Lesson 2.3.610 Minuten
Notes for Lesson 2.3.710 Minuten

7 AufgabenInsgesamt 90 Minuten

Graded assignment for week 330 Minuten
Practice assignment for Lesson 2.3.110 Minuten
Practice assignment for Lesson 2.3.210 Minuten
Practice assignment for Lesson 2.3.310 Minuten
Practice assignment for Lesson 2.3.410 Minuten
Practice assignment for Lesson 2.3.510 Minuten
Practice assignment for Lesson 2.3.610 Minuten

3 Unbewertete LaboreInsgesamt 60 Minuten

A Kalman predictor20 Minuten
A Kalman smoother20 Minuten
Steady-state Kalman filter20 Minuten

A popular application of Kalman filters is to track (usually non-cooperating) targets. This week, you will learn how to implement standard and specialized Kalman filters suited for target tracking.

Das ist alles enthalten

6 Videos6 Lektüren6 Aufgaben2 Unbewertete Labore

6 VideosInsgesamt 107 Minuten

2.4.1: Some unique features of the target-tracking application24 Minuten
2.4.2: Tracking with polar measurements and a Cartesian state16 Minuten
2.4.3: The interacting-multiple-model Kalman filter26 Minuten
2.4.4: Implementing the IMM Kalman filter in Octave21 Minuten
2.4.5: Steady-state alpha-beta-gamma target-tracking filters18 Minuten
2.4.6 :Summary of "Target-tracking application using a linear Kalman filter" module plus next steps3 Minuten