A MIMO Version of the Reed-Yu Detector and Its Connection to the Wilks Lambda and Hotelling $T^2$ Statistics

In this paper we study the problem of detecting a known signal transmitted over a MIMO channel of unknown complex gains and additive noise of unknown covariance. The problem arises in many contexts, including transmit-receiver synchronization. We derive the exact probability distribution for a generalized likelihood ratio (GLR) statistic, and establish the connection between this statistic and the Wilks Lambda and Hotelling $T^2$ statistics. We give alternatives to the GLR statistic, which include the Bartlett-Nanda-Pillai trace, the Lawley-Hotelling trace, and the Roy maximum eigenvalue statistics, each of which is favored under special conditions on the actual MIMO channel. For example, if the channel is an incoherent scattering channel, then the competition for greatest power is among the Bartlett-Nanda-Pillai, Lawley-Hotelling, and GLR statistics. If it is a coherent channel that supports a propagating wave, then Roy's test is more powerful. We discuss the null distribution theory of the GLR at length to show how it may be used to accurately predict false alarm probabilities.

[1]  Philippe Loubaton,et al.  Asymptotic analysis of a GLR test for detection with large sensor arrays: New results , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  R. Butler SADDLEPOINT APPROXIMATIONS WITH APPLICATIONS. , 2007 .

[3]  E. Lehmann Testing Statistical Hypotheses , 1960 .

[4]  C. Khatri On Certain Distribution Problems Based on Positive Definite Quadratic Functions in Normal Vectors , 1966 .

[5]  Louis L. Scharf,et al.  Distribution results for a multi-rank version of the Reed-Yu detector , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

[6]  Xiaoli Yu,et al.  Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution , 1990, IEEE Trans. Acoust. Speech Signal Process..

[7]  Peter A. Parker,et al.  Temporal Synchronization of MIMO Wireless Communication in the Presence of Interference , 2010, IEEE Transactions on Signal Processing.

[8]  C. Khatri Distribution of the Largest or the Smallest Characteristic Root Under Null Hypothesis Concerning Complex Multivariate Normal Populations , 1964 .

[9]  A. J. Collins,et al.  Introduction To Multivariate Analysis , 1981 .

[10]  I. Johnstone MULTIVARIATE ANALYSIS AND JACOBI ENSEMBLES: LARGEST EIGENVALUE, TRACY-WIDOM LIMITS AND RATES OF CONVERGENCE. , 2008, Annals of statistics.

[11]  Y. Chikuse,et al.  Partial differential equations for hypergeometric functions of complex argument matrices and their applications , 1976 .

[12]  Philippe Loubaton,et al.  Large System Analysis of a GLRT for Detection With Large Sensor Arrays in Temporally White Noise , 2015, IEEE Transactions on Signal Processing.

[13]  A. James Distributions of Matrix Variates and Latent Roots Derived from Normal Samples , 1964 .

[14]  Ronald W. Butler,et al.  Approximation of power in multivariate analysis , 2005, Stat. Comput..

[15]  S. S. Wilks CERTAIN GENERALIZATIONS IN THE ANALYSIS OF VARIANCE , 1932 .

[16]  R. Butler,et al.  Limiting saddlepoint relative errors in large deviation regions under purely Tauberian conditions , 2019, Bernoulli.