Detection of signals by information theoretic criteria

A new approach is presented to the problem of detecting the number of signals in a multichannel time-series, based on the application of the information theoretic criteria for model selection introduced by Akaike (AIC) and by Schwartz and Rissanen (MDL). Unlike the conventional hypothesis testing based approach, the new approach does not requite any subjective threshold settings; the number of signals is obtained merely by minimizing the AIC or the MDL criteria. Simulation results that illustrate the performance of the new method for the detection of the number of signals received by a sensor array are presented.

[1]  M. Bartlett.  A Note on the Multiplying Factors for Various χ2 Approximations , 1954 .

[2]  D. Lawley.  TESTS OF SIGNIFICANCE FOR THE LATENT ROOTS OF COVARIANCE AND CORRELATION MATRICES , 1956 .

[3]  T. W. Anderson.  ASYMPTOTIC THEORY FOR PRINCIPAL COMPONENT ANALYSIS , 1963 .

[4]  G. Wahba.  On the Distribution of Some Statistics Useful in the Analysis of Jointly Stationary Time Series , 1968 .

[5]  Robert N. McDonough,et al.  Detection of signals in noise , 1971 .

[6]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[7]  V. Pisarenko.  The Retrieval of Harmonics from a Covariance Function , 1973 .

[8]  H. Akaike.  A new look at the statistical model identification , 1974 .

[9]  N. Owsley.  Spectral Signal Set Extraction , 1977 .

[10]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[11]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[12]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[13]  B. G. Quinn,et al.  The determination of the order of an autoregression , 1979 .

[14]  Georges Bienvenu,et al.  Adaptivity to background noise spatial coherence for high resolution passive methods , 1980, ICASSP.

[15]  S. DeGraaf,et al.  Improving the resolution of bearing in passive sonar arrays by eigenvalue analysis , 1981 .

[16]  S. Kung,et al.  Improved Pisarenko's sinusoidal spectrum estimate via SVD subspace approximation methods , 1982, 1982 21st IEEE Conference on Decision and Control.

[17]  E. Hannan,et al.  The determination of optimum structures for the state space representation of multivariate stochastic processes , 1982 .

[18]  R. Kumaresan,et al.  Estimating the parameters of exponentially damped sinusoids and pole-zero modeling in noise , 1982 .

[19]  Thomas Kailath,et al.  Retrieving the poles from the natural response by eigenstructure method , 1983, The 22nd IEEE Conference on Decision and Control.

[20]  J. Cadzow,et al.  An Algebraic Approach to Superresolution Array Processing , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[21]  J. Rissanen.  A UNIVERSAL PRIOR FOR INTEGERS AND ESTIMATION BY MINIMUM DESCRIPTION LENGTH , 1983 .

[22]  R. Kumaresan,et al.  Estimating the Angles of Arrival of Multiple Plane Waves , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[23]  T. Kailath,et al.  Spatio-temporal spectral analysis by eigenstructure methods , 1984 .

[24]  Alfred M. Bruckstein,et al.  The resolution of overlapping echos , 1985, IEEE Trans. Acoust. Speech Signal Process..