On the Fisher information matrix for multivariate elliptically contoured distributions

The Slepian-Bangs formula provides a very convenient way to compute the Fisher information matrix (FIM) for Gaussian distributed data. The aim of this letter is to extend it to a larger family of distributions, namely elliptically contoured (EC) distributions. More precisely, we derive a closed-form expression of the FIM in this case. This new expression involves the usual term of the Gaussian FIM plus some corrective factors that depend only on the expectations of some functions of the so-called modular variate. Hence, for most distributions in the EC family, derivation of the FIM from its Gaussian counterpart involves slight additional derivations. We show that the new formula reduces to the Slepian-Bangs formula in the Gaussian case and we provide an illustrative example with Student distributions on how it can be used

When each continuous operator is regular, II

The following theorem is essentially due to L.~Kantorovich and B. Vulikh and it describes one of the most important classes of Banach lattices between which each continuous operator is regular. {\bf Theorem 1.1.} {\sl Let $E$ be an arbitrary L-space and $F$ be an arbitrary Banach lattice with Levi norm. Then ${\cal L}(E,F)={\cal L}^r(E,F),\ (\star)$ that is, every continuous operator from $E$ to $F$ is regular.} In spite of the importance of this theorem it has not yet been determined to what extent the Levi condition is essential for the validity of equality $(\star)$. Our main aim in this work is to prove a converse to this theorem by showing that for a Dedekind complete $F$ the Levi condition is necessary for the validity of $(\star)$. As a sample of other results we mention the following. {\bf Theorem~3.6.} {\sl For a Banach lattice $F$ the following are equivalent: {\rm (a)} $F$ is Dedekind complete; {\rm (b)} For all Banach lattices $E$, the space ${\cal L}^r(E,F)$ is a Dedekind complete vector lattice; {\rm (c)} For all L-spaces $E$, the space ${\cal L}^r(E,F)$ is a vector lattice.

Spectrally Limited Periodic Waveforms for HF OTHR Applications

The problem of a constant modulus (CM) continuous wave (CW) waveform design with the thumb-tack ambiguity function that meets the NTIA RSEC requirements is addressed. The ad-hoc and alternating projection techniques are proposed to modify the spectrum of a prototype waveform to meet the NTIA RSEC requirements, retaining the "thumb-tack" property of the original waveform ambiguity function. Periodic binary frequency shift keying (BFSK), Costas FSK, and noise-like waveforms are modified to meet CM and NTIA RSEC requirements. Introduced examples demonstrate the spectrum modification consequences and the proposed technique's efficiency for generating the CM spectrum-controlled waveforms with the thumb-tack ambiguity function

Invariance properties of the likelihood ratio for covariance matrix estimation in some complex elliptically contoured distributions

The likelihood ratio (LR) for testing if the covariance matrix of the observation matrix X is R has some invariance properties that can be exploited for covariance matrix estimation purposes. More precisely, it was shown in Abramovich et al.(2004, 2007, 2007) that, in the Gaussian case, LR(\R0|X), where R0 stands for the true covariance matrix of the observations \X, has a distribution which does not depend on R0 but only on known parameters. This paved the way to the expected likelihood (EL) approach, which aims at assessing, and possibly enhancing the quality of any covariance matrix estimate (CME) by comparing its LR to that of R0. Such invariance properties of LR (R0|X) were recently proven for a class of elliptically contoured distributions (ECD) in Abramovich and Besson (2013) and Bession and Abramovich (2013) where regularized CME were also presented. The aim of this paper is to derive the distribution of LR(R0|X) for other classes of ECD not covered yet, so as to make the EL approach feasible for a larger class of distributions

Sensitivity analysis of likelihood ratio test in K distributed and/or Gaussian noise

In a recent letter we addressed the problem of detecting a fluctuating target in $K$ distributed noise using multiple coherent processing intervals. It was shown through simulations that the performance of the likelihood ratio test is dominated by the snapshot which corresponds to the minimal value of the texture. However, for this particular snapshot the clutter to thermal noise ratio is not large and hence thermal noise cannot be neglected. In the present paper, we continue our investigation with a view to consider detection in a mixture of $K$ distributed and Gaussian noise. Towards this end we study the sensitivity of our previously derived detectors. First, we provide stochastic representations that allow to evaluate their performance in $K$ distributed noise only or Gaussian noise only. Then, their robustness to a mixture is assessed

Toeplitz Inverse Eigenvalue Problem: Application to the Uniform Linear Antenna Array Calibration

The inverse Toeplitz eigenvalue problem (ToIEP) concerns finding a vector that specifies the real-valued symmetric Toeplitz matrix with the prescribed set of eigenvalues. Since phase "calibration" errors in uniform linear antenna arrays (ULAs) do not change the covariance matrix eigenvalues and the moduli of the covariance matrix elements, we formulate a number of the new ToIEP problems of the Hermitian Toeplitz matrix reconstruction, given the moduli of the matrix elements and the matrix eigenvalues. We demonstrate that for the real-valued case, only two solutions to this problem exist, with the "non-physical" one that in most practical cases could be easily disregarded. The computational algorithm for the real-valued case is quite simple. For the complex-valued case, we demonstrate that the family of solutions is broader and includes solutions inappropriate for calibration. For this reason, we modified this ToIEP problem to match the covariance matrix of the uncalibrated ULA. We investigate the statistical convergence of the ad-hoc algorithm with the sample matrices instead of the true ones. The proposed ad-hoc algorithms require the so-called "strong" or "argumental" convergence, which means a large enough required sample volume that reduces the errors in the estimated covariance matrix elements. Along with the ULA arrays, we also considered the fully augmentable minimum redundancy arrays that generate the same (full) set of covariance lags as the uniform linear arrays, and we specified the conditions when the ULA Toeplitz covariance matrix may be reconstructed given the M-variate MRA covariance matrix.Comment: 27 pages, 40 figure

Interval-based maximum likelihood benchmark for adaptive second-order asynchronous CCI cancellation

© Copyright 2007 IEEEA potential usefulness of a priori time-of-arrival (TOA) information for asynchronous co-channel interference (CCI) cancellation is addressed. An interval-based maximum likelihood (ML) benchmark is developed and compared to the averaged ML benchmark and regularized second-order semi-blind (SB) solution that do not use the TOA information. It is found that in short burst scenarios the SB algorithm demonstrates performance that is close to the interval-based ML benchmark. Furthermore, it is shown that SB may outperform the ML benchmarks in the ML breakdown situation. For longer bursts, the known TOA information can significantly improve the performance.Kuzminskiy, A.M. and Abramovich, Y.I