Linear Kalman Filters and Target Tracking

Tracking moving objects accurately in the presence of noisy sensor data is a fundamental challenge in robotics, aerospace, and autonomous systems. This text-based course guides you through the foundational mathematics of the linear Kalman filter, showing you how to predict and update state estimates for real-world target tracking. What you'll learn: - Understand the core mathematical concepts of probability, covariance, and state-space models. - Derive the step-by-step equations of the linear Kalman filter from first principles. - Implement target tracking algorithms using modern Python programming practices. - Model physical motion and sensor noise to filter out measurement uncertainty. - Analyze filter performance and tune covariance matrices for optimal tracking results. You will start with essential definitions and linear algebra basics, progress to the formal derivation of the filter equations, and conclude with practical tracking scenarios written in clean, modern code. This course is designed for beginners in state estimation; a basic understanding of algebra and programming is helpful, but no prior Kalman filter experience is required. Start reading today to build a solid foundation in target tracking and state estimation.