Conjugate Gradient Parametric Detection of Multichannel Signals

Jiang, Chaoshu; Li, Hongbin; Rangaswamy, Muralidhar

doi:10.1109/taes.2012.6178076

Cited by 19 publications

(12 citation statements)

References 15 publications

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“…This is generally infeasible, consequently there is a rich literature on reducing the number of samples by exploiting prior information [2] [3]. Many of these techniques involves exploiting the low-rank nature of the covariance matrix, such as [4] and [5].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed compressive sensing with dictionary learning (CSDL) algorithm is compared with Multi-stage Wiener filter (MWF) [4], Conjugate Gradient Parametric Adaptive Matched Filter (CGPAMF) [5], Principal Component Inverse (PCI) [9], and Recursive Gram-Schmidt orthonormal basis selection algorithm (RGS). Numerical results based on the Knowledge Aided Sensor Signal Processing and Expert Reasoning (KASSPER) dataset [10] show that CSDL is more accurate than other algorithms with low sample support.…”

Section: Introductionmentioning

confidence: 99%

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Compressive radar clutter subspace estimation using dictionary learning

Bai

Roy

Rangaswamy

2013

2013 IEEE Radar Conference (RadarCon13)

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Abstract-Space-Time Adaptive Processing (STAP) based on matched filter processing in the presence of additive clutter (modeled as colored noise) requires knowledge of the clutter covariance matrix. In practice, this is estimated via the sample covariance matrix using samples from the neighboring range bins around the reference bin. By applying compressive sensing, the number of training samples needed to estimate the covariance matrix can be significantly reduced, provided that the basis mismatch problem, inherent to compressive sensing can be mitigated. This paper presents an adaptive approach to choosing the best sparsifying basis, using dictionary learning to estimate the radar clutter subspace. Numerical results show that the proposed algorithm achieves the desired reduction in training samples, and is more accurate than previous reduced-rank algorithm baseline.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compressive radar clutter subspace estimation using dictionary learning

Bai

Roy

Rangaswamy

2013

2013 IEEE Radar Conference (RadarCon13)

Self Cite

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“…. , l s + K, are independent and identically distributed complex normal random vectors with zero mean and covariance matrix M ∈ C N×N ; observe that we do not use a priori information about the disturbance (see, for instance, [26][27][28]), and 5…”

Section: Problem Formulationmentioning

confidence: 99%

Radar detection and range estimation using oversampled data

Aubry

Maio

Foglia

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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In this work, we propose two adaptive receivers achieving enhanced range estimation capability through a joint exploitation of the oversampling and the spillover of target energy in adjacent range samples. To this end, a proper discrete-time model for the received signal is introduced. Then, the generalized likelihood ratio test (GLRT) and the so-called two-step GLRT are derived and assessed. The performance analysis, conducted using both simulated data and real recorded datasets, is aimed at assessing the effectiveness of proposed solutions, also in comparison with existing detectors sharing range estimation capabilities. The illustrative examples highlight that better detection performance and increased range estimation accuracy can be achieved by exploiting the oversampling at the price of an additional processing cost.

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“…where α is assumed deterministic but unknown, v ∈ ℂ N×1 is the nominal steering vector with ||v|| = 1, n i , i = l − N s , …, l + N s and n k , k = 1, …, K, are independent complex normal random vectors with zero mean and covariance matrix [observe that we do not use any a priori information about the disturbance (see, for instance, [23][24][25]). ]…”

Section: Problem Formulationmentioning

confidence: 99%

A radar detector with enhanced range estimation capabilities for partially homogeneous environment

Aubry¹,

Maio

Foglia

et al. 2014

IET Radar, Sonar & Navigation

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In this study, the authors derive an adaptive decision scheme with enhanced range estimation capabilities for point-like targets in partially homogeneous environments. To this end, the authors jointly exploit the oversampling of the received signal and the spillover of target energy to consecutive range samples. A suitable discrete-time model for the received signal is introduced and the so-called two-step generalised likelihood ratio test is derived and assessed. The performance analysis, conducted resorting to both simulated data and real recorded datasets, is aimed at assessing the effectiveness of the proposed solution, also in comparison with the existing detectors with range estimation capabilities. The illustrative examples highlight that better detection performances and increased range estimation accuracy can be achieved exploiting oversampling at the price of an additional processing cost.

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Conjugate Gradient Parametric Detection of Multichannel Signals

Cited by 19 publications

References 15 publications

Compressive radar clutter subspace estimation using dictionary learning

Compressive radar clutter subspace estimation using dictionary learning

Radar detection and range estimation using oversampled data

A radar detector with enhanced range estimation capabilities for partially homogeneous environment

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