Bayesian Complex Amplitude Estimation and Adaptive Matched Filter Detection in Low-Rank Interference

Dogandzic, A.; Zhang, B.

doi:10.1109/tsp.2006.887151

Cited by 15 publications

(4 citation statements)

References 32 publications

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“…Several methods can be utilized to estimate the MRF parameters, such as iterative condition model (ICM) [20], [21] or the high confidence first (HCF) model [22]. Some ICM models require iterative computation while HCF models use the given data to estimate parameters to describe the image model.…”

Section: Conditions Of Skip Modementioning

confidence: 99%

Efficient Inter Mode Prediction Based on Model Selection and Rate Feedback for H.264/AVC

Lee

Tsai

Wang

et al. 2011

IEEE Trans. Circuits Syst. Video Technol.

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H.264/AVC is a standard developed for various lowcomplexity video applications and high-definition television. To improve coding performance, H.264/AVC may optionally adopt the rate-distortion optimization (RDO) method to find the best encoding mode among various inter and intra modes. However, the exhaustive RDO search among different modes increases the H.264/AVC encoder complexity and limits its application. In this paper, we propose an inter mode prediction algorithm for P slices based on spatial and temporal consistency analysis to reduce the complexity of the RDO computation. We apply the stochastic method to analyze the spatial consistency and use rate information for temporal consistency. The experimental results show a 0.03 peak signal-to-noise ratio loss, a 0.87% bit rate increase, and a 58.39% encoding time reduction on average.Index Terms-H.264/AVC, inter mode decision, spatialtemporal, stochastic analysis.

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Section: Conditions Of Skip Modementioning

confidence: 99%

Efficient Inter Mode Prediction Based on Model Selection and Rate Feedback for H.264/AVC

Lee

Tsai

Wang

et al. 2011

IEEE Trans. Circuits Syst. Video Technol.

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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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“…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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Bayesian Complex Amplitude Estimation and Adaptive Matched Filter Detection in Low-Rank Interference

Cited by 15 publications

References 32 publications

Efficient Inter Mode Prediction Based on Model Selection and Rate Feedback for H.264/AVC

Efficient Inter Mode Prediction Based on Model Selection and Rate Feedback for H.264/AVC

An Improved Low Rank Detector in the High Dimensional Regime

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

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