Stochastic Modeling of Sensors

This lecture covers the stochastic modeling of inertial sensors used in various applications like phones, watches, and unmanned aerial vehicles. It introduces the Generalized Method of Wavelet Moments (GMWM) for optimal fusion with GPS. The lecture explains three search architectures: Serial Search, Parallel Code-phase Search, and Parallel Frequency Search. It discusses the software platform for stochastic calibration of sensors and the challenges in frequency search. The presentation delves into the drawbacks of large FFTs, frequency steps, and search spans, proposing solutions like pre-accumulations and zero-padding. It also addresses the impact of frequency errors on signal detection and the trade-offs in coherent integration time.