Complex Signal Amplitude Estimation and Adaptive Detection in Unknown Low-rank Interference

Dogandzic, A.; Zhang, Benhong

doi:10.1109/acssc.2006.355166

Cited by 6 publications

(9 citation statements)

References 22 publications

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“…Other LR detectors have also been developed in the literature, see e.g. [6][7][8]. So far from our knowledge, the exact distribution of T under both H0 and H1 remains unknown.…”

Section: Introductionmentioning

confidence: 99%

An Improved Low Rank Detector in the High Dimensional Regime

Vallet

Ginolhac

Pascal

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper introduces an improved Low Rank Adaptive Normalized Matched Filter (LR-ANMF) detector in a high dimensional (HD) context where the observation dimension is large and of the same order of magnitude than the sample size. To that end, the statistical analysis of the LR-ANMF, in a context where the target signal is disturbed by a spatially correlated Gaussian clutter and a spatially white Gaussian noise, is addressed. More specifically, the asymptotic distribution under the null hypothesis is derived, in the regime where both the dimension M of the observations and the number N of samples converge to infinity at the same rate and when the clutter covariance matrix has fixed rank K. In particular, it is shown that the LR-ANMF test statistic does not exhibit the CFAR property in the previous asymptotic regime. A correction to the LR-ANMF test is then proposed to ensure the asymptotic CFAR property, providing the improved LR-ANMF, termed as HD-LR-ANMF. Its asymptotic distribution is derived under both the null and the alternative hypotheses. Numerical simulations illustrate the fact that, despite the asymptotic nature of the analysis, the results obtained are accurate for reasonable values of M, N .

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“…Other LR detectors have also been developed in the literature, see e.g. [6][7][8]. So far from our knowledge, the exact distribution of T under both H0 and H1 remains unknown.…”

Section: Introductionmentioning

confidence: 99%

An Improved Low Rank Detector in the High Dimensional Regime

Vallet

Ginolhac

Pascal

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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show abstract

“…In this framework, many methods of detector designing have been derived by authors [6][7][8][9][10][11][12][13][14]. Richmond has given the theoretical performance analyses of different detectors in the case that covariance mismatch, and they gave the reasons of performance loss of detectors under nonhomogeneity [5].…”

Section: Introductionmentioning

confidence: 99%

“…The test based on Bayesian has been derived by [6,7,8] with Wishart or anti-Wishart priors information. Literature [6] gives a model of the simple interference covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Detection of Spread Targets in Nonhomogeneous Environments: A Bayesian Approach

Meng

Niu

2012

2012 International Conference on Computer Science and Service System

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the problem of adaptive detection of spatially distributed targets or targets embedded in no homogeneous clutter with unknown covariance matrix is studied. At first, assume the clutter is complex circular zero-mean Gaussian clutter with an unknown positive definite covariance matrix, and it is independent of the covariance matrix vector under test; the secondary data are assumed to be random, then the properties of complex Wishart distributed is researched. Next, the Generalized Likelihood Ratio Test (GLRT) decision statistic based on Bayesian methods is derived, and then the numerical results are presented by means of Monte Carlo simulation strategy. Assume that cells of signal components are available, in this context, the simulation results highlight that the influence of different numbers of secondary data on detection performance, finally the influence of dispersion exponent on detection performance is studied.

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“…By plugging this estimate in the LR-NMF, we obtain the so-called LR Adaptive Normalized Matched Filter (LR-ANMF). Other LR adaptive detectors have also been developed and can be found in [22], April 1, 2016 DRAFT [23], [24]. Unfortunately for all these detectors and in particular for the LR-ANMF, the theoretical Pfa and Pd are not derived in the literature.…”

Section: Introductionmentioning

confidence: 99%

Approximate distribution of the low-rank adaptive normalized matched filter test statistic under the null hypothesis

Ginolhac¹,

Forster

2016

IEEE Trans. Aerosp. Electron. Syst.

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International audienceIn this paper, we propose to derive an approximate theoretical distribution under the null hypothesis of the Low-Rank Adaptive Normalized Matched Filter (LR-ANMF). This detector is used to detect a target when the disturbance is composed of a Low-Rank Gaussian contribution (called clutter) and an Additive White Gaussian Noise (AWGN). In the LR-ANMF, the estimated covariance matrix is replaced by the estimated orthogonal projector onto the subspace clutter. The method to derive this distribution is based on a perturbation analysis and assumes that the steering vector is far from the clutter and a large Clutter-to-Noise Ratio (CNR). Simulations on a STAP example validate our theoretical result and the impact of both hypotheses is also studied

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Complex Signal Amplitude Estimation and Adaptive Detection in Unknown Low-rank Interference

Cited by 6 publications

References 22 publications

An Improved Low Rank Detector in the High Dimensional Regime

An Improved Low Rank Detector in the High Dimensional Regime

Adaptive Detection of Spread Targets in Nonhomogeneous Environments: A Bayesian Approach

Approximate distribution of the low-rank adaptive normalized matched filter test statistic under the null hypothesis

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