3,176 research outputs found
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
Adaptive Radar Detection of a Subspace Signal Embedded in Subspace Structured plus Gaussian Interference Via Invariance
This paper deals with adaptive radar detection of a subspace signal competing
with two sources of interference. The former is Gaussian with unknown
covariance matrix and accounts for the joint presence of clutter plus thermal
noise. The latter is structured as a subspace signal and models coherent pulsed
jammers impinging on the radar antenna. The problem is solved via the Principle
of Invariance which is based on the identification of a suitable group of
transformations leaving the considered hypothesis testing problem invariant. A
maximal invariant statistic, which completely characterizes the class of
invariant decision rules and significantly compresses the original data domain,
as well as its statistical characterization are determined. Thus, the existence
of the optimum invariant detector is addressed together with the design of
practically implementable invariant decision rules. At the analysis stage, the
performance of some receivers belonging to the new invariant class is
established through the use of analytic expressions
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
Matched direction detectors and estimators for array processing with subspace steering vector uncertainties
In this paper, we consider the problem of estimating and detecting a signal whose associated spatial signature is known to lie in a given linear subspace but whose coordinates in this subspace are otherwise unknown, in the presence of subspace interference and broad-band noise. This situation arises when, on one hand, there exist uncertainties about the steering vector but, on the other hand, some knowledge about the steering vector errors is available. First, we derive the maximum-likelihood estimator (MLE) for the problem and compute the corresponding Cramer-Rao bound. Next, the maximum-likelihood estimates are used to derive a generalized likelihood ratio test (GLRT). The GLRT is compared and contrasted with the standard matched subspace detectors. The performances of the estimators and detectors are illustrated by means of numerical simulations
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
A novel approach to robust radar detection of range-spread targets
This paper proposes a novel approach to robust radar detection of
range-spread targets embedded in Gaussian noise with unknown covariance matrix.
The idea is to model the useful target echo in each range cell as the sum of a
coherent signal plus a random component that makes the signal-plus-noise
hypothesis more plausible in presence of mismatches. Moreover, an unknown power
of the random components, to be estimated from the observables, is inserted to
optimize the performance when the mismatch is absent. The generalized
likelihood ratio test (GLRT) for the problem at hand is considered. In
addition, a new parametric detector that encompasses the GLRT as a special case
is also introduced and assessed. The performance assessment shows the
effectiveness of the idea also in comparison to natural competitors.Comment: 28 pages, 8 figure
A Unifying Framework for Adaptive Radar Detection in Homogeneous plus Structured Interference-Part II: Detectors Design
This paper deals with the problem of adaptive multidimensional/multichannel
signal detection in homogeneous Gaussian disturbance with unknown covariance
matrix and structured (unknown) deterministic interference. The aforementioned
problem extends the well-known Generalized Multivariate Analysis of Variance
(GMANOVA) tackled in the open literature. In a companion paper, we have
obtained the Maximal Invariant Statistic (MIS) for the problem under
consideration, as an enabling tool for the design of suitable detectors which
possess the Constant False-Alarm Rate (CFAR) property. Herein, we focus on the
development of several theoretically-founded detectors for the problem under
consideration. First, all the considered detectors are shown to be function of
the MIS, thus proving their CFARness property. Secondly, coincidence or
statistical equivalence among some of them in such a general signal model is
proved. Thirdly, strong connections to well-known simpler scenarios found in
adaptive detection literature are established. Finally, simulation results are
provided for a comparison of the proposed receivers.Comment: Submitted for journal publicatio
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