MATLAB Implementation of Doppler Velocity Measurement

Doppler velocity measurement is a technique that utilizes the Doppler effect to determine the relative speed of objects. The Doppler effect occurs when there is relative motion between a signal source and receiver, causing a shift in the received signal frequency. Autocorrelation frequency estimation serves as a fundamental method for measuring Doppler shifts, enabling accurate estimation of signal frequency and phase characteristics through time-domain correlation analysis. In MATLAB implementations, this typically involves calculating the autocorrelation function of sampled signals using xcorr() or similar functions, followed by peak detection algorithms to identify dominant frequency components. The estimation error analysis forms a critical aspect of Doppler velocity measurement systems, where simulation frameworks incorporate various noise models (Additive White Gaussian Noise) and signal uncertainties to evaluate algorithm robustness. During simulation processes, developers can introduce controlled noise levels and parameter uncertainties to replicate real-world scenarios. Performance metrics such as Mean Square Error (MSE) and Cramer-Rao Lower Bound (CRLB) calculations help quantify estimation accuracy. Algorithm improvement strategies often involve comparative analysis between conventional methods (like periodogram-based approaches) and advanced techniques (such as MUSIC or ESPRIT algorithms) through Monte Carlo simulations and statistical validation of results.