The Asymptotic Cramér-Rao Bound for 2-D Superimposed Exponential Signals
Multidimensional Systems and Signal Processing
Frequency estimation via multiple lags of correlations in the presence of broadband colored noise
Signal Processing - Special section: Distributed source coding
Signal Processing
A robust and computationally efficient subspace-based fundamental frequency estimator
IEEE Transactions on Audio, Speech, and Language Processing
Noncoherent MIMO radar for location and velocity estimation: more antennas means better performance
IEEE Transactions on Signal Processing
Generalization of iterative Fourier interpolation algorithms for single frequency estimation
Digital Signal Processing
Damped sinusoidal signals parameter estimation in frequency domain
Signal Processing
Component statistical analysis of second order hidden periodicities
Digital Signal Processing
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The problem of estimating the parameters of complex-valued sinusoidal signals (cisoids, for short) from data corrupted by colored noise occurs in many signal processing applications. We present a simple formula for the asymptotic (large-sample) Cramer-Rao bound (CRB) matrix associated with this problem. The maximum likelihood method (MLM), which estimates both the signal and noise parameters, attains the performance corresponding to the asymptotic CRB, as the sample length increases. More interestingly, we show that a computationally much simpler nonlinear least-squares method (NLSM), which estimates the signal parameters only, achieves the same performance in large samples