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This page is a copy of research/systems_and_control/signalproc/topics/fundspec (Wed, 31 Aug 2022 15:09:07)

Fundamental problems in temporal/spatial spectral estimation, including array processing

Following is an enumeration of the research problems under consideration, along with a short explanation.

Parameter estimation of sinusoidal signals using decimation
Decimation of the data, making use of some a priori information, can significantly improve the resolution and accuracy of the methods for estimating the parameters of damped or undamped sine waves.
Matched-filterbank spectral estimation
Introducing a matched-filterbank class of spectral estimators, casting some existing estimators into that framework, and establishing their statistical properties.
Parameter estimation of exponential signals with time varying envelope
New methods and new tools for the above task (such as nonlinear least squares, polar decomposition-based methods etc), and their statistical properties.
Novel estimators of covariance sequences
New procedures for estimating the covariance sequence of stationary signals, with better properties than the usual sample covariance estimators.
Covariance matching techniques for array signal processing
Studying the properties of such techniques and applying them to a variety of problems in communications, underwater acoustics, and radar.
Non-parametric spectral estimation
New optimally smoothed nonparametric spectral estimators; Lower bounds on the performance achievable in a nonparametric spectral analysis exercise; Spectral smoothing based on cepstrum thresholding.

Selected references:

X Tan, W Roberts, J Li and P Stoica, Sparse learning via iterative minimization with application to MIMO radar imaging. IEEE Trans Signal Process, vol 59, 1088-1101, 2011.
P Stoica, P Babu and J Li, SPICE: a sparse covariance-based estimation method for array processing. IEEE Trans Signal Process, vol 59, 629-638, 2011.

  1. Comments on "Iterative Estimation of Sinusoidal Signal Parameters". Prabhu Babu and Peter Stoica. In IEEE Signal Processing Letters, volume 17, number 12, pp 1022-1023, 2010. (DOI).
  2. MIMO Radar: Diversity means superiority. Jian Li and Peter Stoica. In MIMO Radar Signal Processing, pp 1-64, John Wiley & Sons, Hoboken, NJ, 2009.
  3. MIMO Radar Signal Processing. Jian Li and Peter Stoica (eds). John Wiley & Sons, Hoboken, NJ, 2009.
  4. On nonparametric estimation of 2-D smooth spectra. Niclas Sandgren and Peter Stoica. In IEEE Signal Processing Letters, volume 13, number 10, pp 632-635, 2006. (DOI).
  5. The waterbed effect in spectral estimation. Peter Stoica, J. Li, and B. Ninness. In IEEE Signal Processing Mag., pp 88-90, 2004.

Updated  2022-08-31 15:09:07 by Victor Kuismin.