Knowledge-aided Bayesian covariance matrix estimation in compound-Gaussian clutter

We address the problem of estimating a covariance matrix R using K samples zk whose covariance matrices are kR, where k are random variables. This problem naturally arises in radar applications in the case of compound-Gaussian clutter. In contrast to the conventional approach which consists in considering R as a deterministic quantity, a knowledge-aided (KA) approach is advocated here, where R is assumed to be a random matrix with some prior distribution. The posterior distribution of R is derived. Since it does not lead to a closed-form expression for the minimum mean-square error (MMSE) estimate of R, both R and k are estimated using a Gibbs-sampling strategy. The maximum a posteriori (MAP) estimator ofR is also derived. It is shown that it obeys an implicit equation which can be solved through an iterative procedure, similarly to the case of deterministic ks, except that KA is now introduced in the iterative scheme. The new estimators are shown to improve over conventional estimators, especially in small sample support

Knowledge-aided covariance matrix estimation and adaptive detection in compound-Gaussian noise

We address the problem of adaptive detection of a signal of interest embedded in colored noise modeled in terms of a compound-Gaussian process. The covariance matrices of the primary and the secondary data share a common structure while having different power levels. A Bayesian approach is proposed here, where both the power levels and the structure are assumed to be random, with some appropriate distributions. Within this framework we propose MMSE and MAP estimators of the covariance structure and their application to adaptive detection using the NMF test statistic and an optimized GLRT herein derived. Some results, also conducted in comparison with existing algorithms, are presented to illustrate the performances of the proposed algorithms. The relevant result is that the solutions presented herein allows to improve the performance over conventional ones, especially in presence of a small number of training data

Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: A Bayesian approach

In this paper, we deal with the problem of adaptive detection of distributed targets embedded in colored noise modeled in terms of a compound-Gaussian process and without assuming that a set of secondary data is available.The covariance matrices of the data under test share a common structure while having different power levels. A Bayesian approach is proposed here, where the structure and possibly the power levels are assumed to be random, with appropriate distributions. Within this framework we propose GLRT-based and ad-hoc detectors. Some simulation studies are presented to illustrate the performances of the proposed algorithms. The analysis indicates that the Bayesian framework could be a viable means to alleviate the need for secondary data, a critical issue in heterogeneous scenarios

An ABORT-like detector with improved mismatched signals rejection capabilities

In this paper, we present a GLRT-based adaptive detection algorithm for extended targets with improved rejection capabilities of mismatched signals. We assume that a set of secondary data is available and that noise returns in primary and secondary data share the same statistical characterization. To increase the selectivity of the detector, similarly to the ABORT formulation, we modify the hypothesis testing problem at hand introducing fictitious signals under the null hypothesis. Such unwanted signals are supposed to be orthogonal to the nominal steering vector in the whitened observation space. The performance assessment, carried out by Monte Carlo simulation, shows that the proposed dectector ensures better rejection capabilities of mismatched signals than existing ones, at the price of a certain loss in terms of detection of matched signals

Direction detector for distributed targets in unknown noise and interference

Adaptive detection of distributed radar targets in homogeneous Gaussian noise plus subspace interference is addressed. It is assumed that the actual steering vectors lie along a fixed and unknown direction of a preassigned and known subspace, while interfering signals are supposed to belong to an unknown subspace, with directions possibly varying from one resolution cell to another. The resulting detection problem is formulated in the framework of statistical hypothesis testing and solved using an ad hoc algorithm strongly related to the generalised likelihood ratio test. A performance analysis, carried out also in comparison to natural benchmarks, is presented

An improved adaptive sidelobe blanker

We propose a two-stage detector consisting of a subspace detector followed by the whitened adaptive beamformer orthogonal rejection test. The performance analysis shows that it possesses the constant false alarm rate property with respect to the unknown covariance matrix of the noise and that it can guarantee a wider range of directivity values with respect to previously proposed two-stage detectors. The probability of false alarm and the probability of detection (for both matched and mismatched signals) have been evaluated by means of numerical integration techniques

Theoretical performance analysis of the W-ABORT detector

In a recent paper we introduced a modification of the adaptive beaniformer orthogonal rejection test (ABORT) for adaptive detection of signals in unknown noise, by supposing under the null hypothesis the presence of signals orthogonal to the nominal steering vector in the whitened observation space. We will refer to this new receiver as the whitened adaptive beamformer orthogonal rejection test (W-ABORT). Through Monte Carlo simulations this new detector was shown to provide better rejection capabilities of mismatched (e.g., sidelobe) signals than existing ones, like ABORT or the adaptive coherence estimator (ACE), but at the price of a certain loss in terms of detection of matched (i.e., mainlobe) signals. The aim of this paper is to provide a theoretical validation of this fact. We consider both the case of distributed targets and point-like targets. We provide a statistical characterization of the W-ABORT test statistic, under the null hypothesis, and for matched and mismatched signals under the alternative hypothesis. For distributed targets, the probability of false alarm and the probability of detection can only be expressed in terms of multi-dimensional integrals, and are thus very complicated to obtain; in contrast, for point-like targets, such probabilities can be easily calculated by numerical integration techniques. The theoretical expressions derived herein corroborate the simulation results obtained previously

Freesound Explorer: Make Music While Discovering Freesound!

Freesound Explorer is a visual interface for exploring Freesound content in a two-dimensional space and creating music by linking content in that space. Freesound Explorer is implemented as a web application which takes advantage of modern web technologies including the Web Audio API and the Web MIDI API. This extended abstract describes Freesound Explorer’s features and provides some technical details about its implementation

GLRT-Based Direction Detectors in Homogeneous Noise and Subspace Interference

In this paper, we derive and assess decision schemes to discriminate, resorting to an array of sensors, between the H0 hypothesis that data under test contain disturbance only (i.e., noise plus interference) and the H1 hypothesis that they also contain signal components along a direction which is a priori unknown but constrained to belong to a given subspace of the observables. The disturbance is modeled in terms of complex normal random vectors plus deterministic interference assumed to belong to a known subspace. We assume that a set of noise-only (secondary) data is available, which possess the same statistical characterization of noise in the cells under test. At the design stage, we resort to either the plain generalized-likelihood ratio test (GLRT) or the two-step GLRT-based design procedure. The performance analysis, conducted resorting to simulated data, shows that the one-step GLRT performs better than the detector relying on the two-step design procedure when the number of secondary data is comparable to the number of sensors; moreover, it outperforms a one-step GLRT-based subspace detector when the dimension of the signal subspace is sufficiently high

Phytoremediation opportunities with alimurgic species in metal-contaminated environments

Alimurgic species are edible wild plants growing spontaneously as invasive weeds in natural grassland and farmed fields. Growing interest in biodiversity conservation projects suggests deeper study of the multifunctional roles they can play in metal uptake for phytoremediation and their food safety when cultivated in polluted land. In this study, the responses of the tap-rooted perennial species Cichorium intybus L., Sonchus oleracerus L., Taraxacum officinaleWeb., Tragopogon porrifolius L. and Rumex acetosa L. were studied in artificially-highly Cd-Co-Cu-Pb-Zn-contaminated soil in a pot-scale trial, and those of T. officinale and R. acetosa in critical open environments (i.e., landfill, ditch sediments, and sides of highly-trafficked roads). Germination was not inhibited, and all species showed appreciable growth, despite considerable increases in tissue metal rates. Substantial growth impairments were observed in C. intybus, T. officinale and T. porrifolius; R. acetosa and S. oleracerus were only marginally affected. Zn was generally well translocated and reached a high leaf concentration, especially in T. officinale (~600 mg/kg dry weight, DW), a result which can be exploited for phytoremediation purposes. The elevated Cd translocation also suggested applications to phytoextraction, particularly with C. intybus, in which leaf Cd reached ~16 mg/kg DW. The generally high root retention of Pb and Cu may allow their phytostabilisation in the medium-term in no-tillage systems, together with significant reductions in metal leaching compared with bare soil. In open systems, critical soil Pb and Zn were associated with heavily trafficked roadsides, although this was only seldom reflected in shoot metal accumulation. It is concluded that a community of alimurgic species can serve to establish an efficient, long-lasting vegetation cover applied for phytoremediation and reduction of soil metal movements in degraded environments. However, their food use is not recommended, since leaf Cd and Pb may exceed EU safety thresholds