Fundamentals of Sensing, Tracking, and Autonomy, Part I

Defining sensors; physical vs virtual sensors. Chronometers, cameras, infrared, laser, temperature, IMU. Sensor mappings, resolution, noise, calibration. Preimages, sources of uncertainty, comparing sensors, stochastic modeling. Multiple sensor readings and networks of sensors. Triangulation principles. Motion models: Discrete time, continuous time, event-based. Linear, complementary, Kalman, Bayesian, and combinatorial filters. Localization and mapping; global positioning systems; tracking humans.