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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.

In \cite{Abramovich04}, it was demonstrated that the likelihood ratio (LR) for multivariate complex Gaussian distribution has the invariance property that can be exploited in many applications. Specifically, the probability density function (p.d.f.) of this LR for the (unknown) actual covariance matrix $\R_{0}$ does not depend on this matrix and is fully specified by the matrix dimension $M$ and the number of independent training samples $T$. Since this p.d.f. could therefore be pre-calculated for any a priori known $(M,T)$, one gets a possibility to compare the LR of any derived covariance matrix estimate against this p.d.f., and eventually get an estimate that is statistically ``as likely'' as the a priori unknown actual covariance matrix. This ``expected likelihood'' (EL) quality assessment allows for significant improvement of MUSIC DOA estimation performance in the so-called ``threshold area'' \cite{Abramovich04,Abramovich07d}, and for diagonal loading and TVAR model order selection in adaptive detectors \cite{Abramovich07,Abramovich07b}. Recently, a broad class of the so-called complex elliptically symmetric (CES) distributions has been introduced for description of highly in-homogeneous clutter returns. The aim of this report is to extend the EL approach to this class of CES distributions as well as to a particularly important derivative of CES, namely the complex angular central distribution (ACG). For both cases, we demonstrate a similar invariance property for the LR associated with the true scatter matrix $\mSigma_{0}$. More precisely, we show that the probability density function (p.d.f.) of the likelihood ratio (LR) for the (unknown) actual scatter matrix $\mSigma_{0}$ does not depend on the latter: it only depends on the density generator for the CES distribution and is distribution-free in the case of ACG distributed data, i.e., it only depends on the matrix dimension $M$ and the number of independent training samples $T$. Furthermore, we derive fixed point regularized covariance matrix estimates using the generalized expected likelihood methodology. This first chapter of this report is devoted to the conventional scenario ($T \geq M$) while Chapter 2 deals with the under-sampled scenario ($T \leq M$). Although the two parts are closely related, each one is self-contained and could be read independently.

We introduce an adaptive technique for the joint mitigation of transients and continuous-wave radio-frequency co-channel interference (CW RFI) in high-frequency (HF) over-the-horizon radars (OTHRs). The performance of this technique is illustrated using data from an operational surface-wave radar (SECAR) and from recent experimental trials with sky-wave (SW) and sky-wave–line-of-sight (SKYLOS) HF OTHRs.

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