Dual Frequency Estimation in Sinusoidal Signals Embedded in Additive White Gaussian Noise

This project involves the design and implementation of a methodology for estimating dual frequencies in sinusoidal signals contaminated by additive white Gaussian noise. We derive the Cramér-Rao Lower Bound (CRLB) for the signal model and develop a nonlinear least squares frequency estimator. The implementation includes numerical validation through comprehensive simulations and performance analysis under various signal-to-noise ratio conditions.