Lectures in course ENV-548: Sensor orientation

Covers the concept of parameter estimation using the least square method.

Introduces the rigorous approach to sensor fusion for modern applications.

Covers the concept of covariance propagation through mathematical equations and calculations.

Covers the concept of least square estimation and its real-world applications.

Explains linearized and non-linear Kalman Filter models and the use of Jacobians.

Discusses the limitations of a linearized Kalman filter and introduces the extended Kalman filter.

Covers the theory and practice of Kalman Filter in Matlab.

Introduces the Kalman Filter, a method to estimate system state from noisy measurements.

Covers the concept of pseudo observations and their application in updating measurements.

Covers the motion model for uniform circular motion in robotics.

Explores non-linear updates with pseudo-observations and coordinate transformations.

Discusses non-linear design models, Cartesian system transformation, Jacobian matrices, and update processes.

Covers the Extended Kalman Filter algorithm with a focus on time-correlated observations and practical implementations.

Explores EKF for 2D motion prediction using INS and GPS, addressing challenges and mathematical solutions for accurate navigation.

Explores EKF for 2D motion prediction with INS and GPS integration.

Covers the Extended Kalman Filter with state augmentation and feedback error control.

Explains the Recursive Least Squares algorithm for updating parameter estimates in linear regression models.

Covers the Recursive Least Squares method for parameter estimation based on observation equations and design matrices.

Explains the Earth Centered Earth Fixed (ECEF) frame and its coordinate systems.