Detection of unknown-frequency sinusoids in noise via autoregressive modeling

A family of autoregressive (AR) detection statistics is introduced and their potential for enhanced detection of unknown-frequency sinusoids in noise is illustrated. AR models applied to both the time data and the correlation data are addressed and compared. The computational complexity of the resulting decision rules is also incorporated in the analysis.

[1]  Don H. Johnson,et al.  Reduction of all-pole parameter estimator bias by successive autocorrelation , 1983, ICASSP.

[2]  C. L. Nikias,et al.  The Covariance Least-Squares Algorithm for Spectral Estimation of Processes of Short Data Length , 1983, IEEE Transactions on Geoscience and Remote Sensing.

[3]  E. Hung,et al.  Simulation experiments to compare the signal detection properties of DFT and MEM spectra , 1981 .

[4]  S.M. Kay,et al.  Spectrum analysis—A modern perspective , 1981, Proceedings of the IEEE.

[5]  G. W. Lank,et al.  A Semicoherent Detection and Doppler Estimation Statistic , 1973, IEEE Transactions on Aerospace and Electronic Systems.

[6]  J. Ketchum,et al.  Signal detection using autoregressive parameters , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Andreas Polydoros,et al.  LPI Detection of Frequency-Hopping Signals Using Autocorrelation Techniques , 1985, IEEE J. Sel. Areas Commun..

[8]  Steven Kay.  Broadband detection of signals with unknown spectra , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Chrysostomos L. Nikias,et al.  Detection of unknown-frequency sinusoids in noise: Spectral versus correlation domain , 1987, IEEE Trans. Acoust. Speech Signal Process..

[10]  Steven Kay.  Robust detection by autoregressive spectrum analysis , 1982 .