Burst sinusoidal frequency estimation with short record length via L1 normed linear predictive modeling

The authors demonstrates that estimating the two sinusoidal frequencies of a burst sinusoidal frequency shift keyed (FSK) waveform via a least squares based linear predictive algorithm may result in significant frequency bias even in the absence of additional noise. Transient effects present from uncertain burst signal location within the analysis window may be detrimental to least squares based frequency estimation methods. Such signal transients are manifested as spikes within the residual vector produced from a linear predictive model based approach to frequency estimation. For such cases, it is demonstrated that a robust estimator, such as an estimator generated via a L ₁ normed error criteria, may produce significantly less biased frequency estimates. Additionally, it is shown that sinusoidal frequency estimates generated via a L₁ normed solution are insensitive to initial signal phase in contrast to least squares based estimates