Exploiting persymmetry for low-rank Space Time Adaptive Processing

International audienceReducing the number of secondary data used to estimate the Covariance Matrix (CM) for Space Time Adaptive Processing (STAP) techniques is still an active research topic. Within this framework, the Low-Rank (LR) structure of the clutter is well-known and the corresponding LR STAP filters have been shown to exhibit a smaller Signal Interference plus Noise Ratio (SINR) loss than classical STAP filters, only 2r secondary data (where r is the clutter rank) instead of 2m (where m is the data size) are required to reach the classical 3 dB SNR loss. By using other features of the radar system, other properties of the CM can be exploited to further reduce the number of secondary data; this is the case for active systems using a symmetrically spaced linear array with constant pulse repetition interval, which results in a persymmetric structure of the noise CM. In this context, we propose to combine this property of the CM and the LR structure of the clutter to perform CM estimation. In this paper, the resulting STAP filter is shown, both theoretically and experimentally, to exhibit good performance with fewer secondary data; 3 dB SINR Loss is achieved with only r secondary data

Adaptive detection using randomly reduced dimension generalized likelihood ratio test

We address the problem of detecting a signal of interest in the presence of Gaussian noise with unknown statistics when the number of training samples available to learn the noise covariance matrix is less than the size of the observation space. Following an idea by Marzetta, a series of K random semi-unitary matrices are applied to the data to achieve dimensionality reduction. Then, the K corresponding generalized likelihood ratios are computed and their median value provides the final detector. We show that the semi-unitary matrices can be replaced by random Gaussian matrices without affecting the final test statistic. The new detector avoids eigenvalue decomposition and is easily amenable to parallel implementation. It is compared to conventional techniques based on diagonal loading of the sample covariance matrix or based on rank reduction through eigenvalue decomposition and is shown to perform well

A Geometric Approach to Covariance Matrix Estimation and its Applications to Radar Problems

A new class of disturbance covariance matrix estimators for radar signal processing applications is introduced following a geometric paradigm. Each estimator is associated with a given unitary invariant norm and performs the sample covariance matrix projection into a specific set of structured covariance matrices. Regardless of the considered norm, an efficient solution technique to handle the resulting constrained optimization problem is developed. Specifically, it is shown that the new family of distribution-free estimators shares a shrinkagetype form; besides, the eigenvalues estimate just requires the solution of a one-dimensional convex problem whose objective function depends on the considered unitary norm. For the two most common norm instances, i.e., Frobenius and spectral, very efficient algorithms are developed to solve the aforementioned one-dimensional optimization leading to almost closed form covariance estimates. At the analysis stage, the performance of the new estimators is assessed in terms of achievable Signal to Interference plus Noise Ratio (SINR) both for a spatial and a Doppler processing assuming different data statistical characterizations. The results show that interesting SINR improvements with respect to some counterparts available in the open literature can be achieved especially in training starved regimes.Comment: submitted for journal publicatio

Dark Matter searches using gravitational wave bar detectors: quark nuggets and newtorites

Many experiments have searched for supersymmetric WIMP dark matter, with null results. This may suggest to look for more exotic possibilities, for example compact ultra-dense quark nuggets, widely discussed in literature with several different names. Nuclearites are an example of candidate compact objects with atomic size cross section. After a short discussion on nuclearites, the result of a nuclearite search with the gravitational wave bar detectors Nautilus and Explorer is reported. The geometrical acceptance of the bar detectors is 19.5 $\rm m^2$ sr, that is smaller than that of other detectors used for similar searches. However, the detection mechanism is completely different and is more straightforward than in other detectors. The experimental limits we obtain are of interest because, for nuclearites of mass less than $10^{-5}$ g, we find a flux smaller than that one predicted considering nuclearites as dark matter candidates. Particles with gravitational only interactions (newtorites) are another example. In this case the sensitivity is quite poor and a short discussion is reported on possible improvements.Comment: published on Astroparticle Physics Sept 25th 2016 replaced fig 1

Synthèse des traitements STAP pour la détection en environnement hétérogène

Cet article synthétise les différents algorithmes spatio-temporels adaptatifs (STAP) développés et/ou utilisés pour la détection en environnement non-homogène. Nous rappelons en premier lieu les causes principales qui peuvent conduire à un environnement hétérogène. Puis nous présentons les stratégies STAP les plus communément utilisées dans de tels environnements