
CIF: Small: ComplexValued Statistical Signal Processing with Dependent DataPI: J.K. Tugnait, 07/01/16  6/30/19, $418,527.Supported by the National Science Foundation Grant CCF1617610 Complexvalued random signals arise in many areas of science and engineering such as communications, radar, sonar, geophysics, oceanography, optics, electromagnetics, and acoustics. If the crosscovariance function of the signal with its complex conjugate vanishes, the signal is called proper, otherwise it is improper. If the underlying signals are improper, much can be gained in performance if they are treated as improper. If it is not known apriori whether a signal of interest is proper or improper, this information must be obtained from its noisy measurements. Existing approaches to determination of propriety are limited to the case where the measurements consist of a sequence of independent random vectors. Practical reallife signals do not typically consist of independent measurement samples. This research focuses on approaches designed to handle dependent data. Novel, efficient approaches are investigated in this research with emphasis on frequencydomain, improper signals, and applications. The signals are modeled as stationary but are not necessarily Gaussian. The following thrusts form the core of this research. (1) Testing for impropriety of dependent multichannel data with arbitrary distribution unlike past work which is limited to independent sequences, typically assumed to be Gaussian. (2) Comparison of random complex signals involving statistical tests to ascertain if two multichannel random signals have the same secondorder statistics. Application of such tests for user authentication in wireless networks is investigated. (3) Detection of multichannel complex signals in noise using a generalized likelihood ratio test formulation is studied, without requiring a structured model or Gaussian assumption. (4) This research also involves reexamination and modification of all aforementioned approaches to be robust with respect to additive or innovations outlier model. J.K. Tugnait, "Graphical lasso for highdimensional complex Gaussian graphical model selection," to be presented at the 2019 IEEE Intern. Conf. Acoustics, Speech & Signal Processing (ICASSP 2019), Brighton, UK, May 1217, 2019. J.K. Tugnait, "Robust spectrumbased comparison of multivariate complex random signals," IEEE Access, vol. 7, pp. 1252112528, Feb. 6, 2019. [pdf] J.K. Tugnait, "On multisensor detection of improper signals," IEEE Trans. Signal Processing, vol. 67, no. 4, pp. 870884, Feb 15, 2019. [pdf] J.K. Tugnait, "On robust comparison of multivariate complex random signals," presented at the 52nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, Oct. 28  Oct. 31, 2018. J.K. Tugnait, "Graphical modeling of highdimensional time series," presented at the 52nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, Oct. 28  Oct. 31, 2018. J.K. Tugnait, "An edge exclusion test for complex Gaussian graphical model selection," in Proc. 2018 IEEE Statistical Signal Processing Workshop (SSP), pp. 678682, Freiburg, Germany, June 1013, 2018. J.K. Tugnait, "An edge exclusion test for graphical modeling of multivariate time series," in Proc. 2018 52nd Annual Conf. on Information Sciences & Systems (CISS), pp. 16, Princeton University, Princeton, NJ, March 2123, 2018. J.K. Tugnait, "Spectrumbased comparison of multivariate complex random signals of unequal lengths," in Proc. 51st Asilomar Conference on Signals, Systems and Computers, pp. 757761, Pacific Grove, CA, Oct. 29  Nov. 1, 2017. J.K. Tugnait and S.A. Bhaskar, "On testing for impropriety of multivariate complexvalued random sequences," IEEE Trans. Signal Processing, vol. 65, no. 11, pp. 29883003, June 1, 2017. J.K. Tugnait, "Multisensor detection of improper signals in improper noise," in Proc. 2017 IEEE Intern. Conf. Acoustics, Speech & Signal Processing (ICASSP 2017), pp. 39393943, New Orleans, Louisiana, March 59, 2017. Author: Jitendra K. Tugnait: tugnajk@eng.auburn.edu Date of latest revision: thu feb 21 2019 