$L_1$-Regularized STAP Algorithms With a Generalized Sidelobe Canceler Architecture for Airborne Radar

Yang, Zhaocheng; Lamare, Rodrigo C. de; Li, Xiang

doi:10.1109/tsp.2011.2172435

Cited by 134 publications

(141 citation statements)

References 33 publications

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“…Moreover, bðlÞ ¼ B H xðlÞ and d 0 ðlÞ ¼ v H t xðlÞ, where xðlÞ denotes the l'th space-time snapshot. [13][14][15] The solution of Eq. (18) can be obtained directly, i.e., noniteratively.…”

Section: -Regularized Admm-stapmentioning

confidence: 99%

“…The complexity of the algorithms can be very high when the size of the problem is large, which is not pragmatic in practice. To effectively solve the optimization problem, the l 1 -regularized recursive least-squares STAP (RLS-STAP) algorithm, 13 the l 1 -regularized least-mean-square STAP algorithm, 14 and the homotopy-STAP algorithm 15 have been proposed. Compared with conventional STAP methods, sparsity-based STAP techniques have been shown to provide high resolution and exhibit better performance than conventional STAP algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Fast ℓ₁-regularized space-time adaptive processing using alternating direction method of multipliers

Qin

Wang

et al. 2017

J. Appl. Remote Sens

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, "Fast l 1 -regularized space-time adaptive processing using alternating direction method of multipliers," J. Appl. Remote Sens. 11(2), 026004 (2017), doi: 10.1117/1.JRS.11.026004. Abstract. Motivated by the sparsity of filter coefficients in full-dimension space-time adaptive processing (STAP) algorithms, this paper proposes a fast l 1 -regularized STAP algorithm based on the alternating direction method of multipliers to accelerate the convergence and reduce the calculations. The proposed algorithm uses a splitting variable to obtain an equivalent optimization formulation, which is addressed with an augmented Lagrangian method. Using the alternating recursive algorithm, the method can rapidly result in a low minimum mean-square error without a large number of calculations. Through theoretical analysis and experimental verification, we demonstrate that the proposed algorithm provides a better output signal-toclutter-noise ratio performance than other algorithms. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: -Regularized Admm-stapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast ℓ₁-regularized space-time adaptive processing using alternating direction method of multipliers

Qin

Wang

et al. 2017

J. Appl. Remote Sens

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“…In the last decades antenna array signal processing was successfully applied to different research areas such as communications [1], [2], audio [3], [4] and radar [5]. In some cases, antenna arrays have structures that can be exploited via multidimensional techniques.…”

Section: Introductionmentioning

confidence: 99%

Generalized sidelobe cancellers for multidimensional separable arrays

Miranda

Costa

Roemer

et al. 2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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The usage of antenna arrays brought innumerable benefits to radio systems in the last decades. Arrays can have multidimensional structures that can be exploited to achieve superior performance and lower complexity. However, the literature has not explored yet all the advantages arising from these features. This paper uses tensors to provide a method to design efficient beamformers for multidimensional antenna arrays. In this work, the generalized sidelobe canceller (GSC) is extended to a multidimensional array to create the proposed R-Dimensional GSC (R-D GSC). The proposed scheme has a lower computational complexity and, under certain conditions, exhibits an improved signal to interference and noise ratio (SINR)

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“…Beamforming is an important topic in array signal processing and has applications in several fields such as RADAR [1], SONAR [2], telecommunications [3] and audio [4]. In the literature, there are several adaptations of Direction of Arrival (DOA) estimation schemes for colored noise scenarios [5][6][7], and once the DOA information is obtained, it can be introduced to the beamformer.…”

Section: Introductionmentioning

confidence: 99%

High accuracy and low complexity adaptive Generalized Sidelobe Cancelers for colored noise scenarios

Miranda

Costa

Antreich

2014

Digital Signal Processing

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The Generalized Sidelobe Canceler (GSC) is a beamforming scheme which is applied in many fields such as audio, RADAR, SONAR and telecommunications. Recently, the adaptive Reduced Rank GSC (RR-GSC) has been proposed for applications with a large number of sensors. Due to its dimensionality reduction step, the adaptive RR-GSC achieves an enhanced performance in comparison with the standard GSC. However, both standard GSC and RR-GSC have their performance drastically degraded in the presence of colored noise. In this paper, we propose to extend further the GSC and the RR-GSC for colored noise scenarios. As shown in this paper, such improvement in colored noise scenarios can be obtained by incorporating a stochastic or a deterministic prewhitening step in the GSC and RR-GSC algorithms. Since the prewhitening increases the computational complexity, a block-wise reduced rank stochastic gradient GSC beamformer is also proposed. The block-wise step allows only one prewhitening step per block while in the previous schemes one per sample was needed. Another proposed advance in colored noise scenarios is the incorporation of the Vandermonde Invariance Transform (VIT). The VIT works as a pre-beamformer which reduces the interferent power of the undesired sources and the colored noise effect. We show by means of simulations the improved results even for highly correlated scenarios.

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$L_1$-Regularized STAP Algorithms With a Generalized Sidelobe Canceler Architecture for Airborne Radar

Cited by 134 publications

References 33 publications

Fast ℓ₁-regularized space-time adaptive processing using alternating direction method of multipliers

Fast ℓ₁-regularized space-time adaptive processing using alternating direction method of multipliers

Generalized sidelobe cancellers for multidimensional separable arrays

High accuracy and low complexity adaptive Generalized Sidelobe Cancelers for colored noise scenarios

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$L_1$-Regularized STAP Algorithms With a Generalized Sidelobe Canceler Architecture for Airborne Radar

Cited by 134 publications

References 33 publications

Fast ℓ1-regularized space-time adaptive processing using alternating direction method of multipliers

Fast ℓ1-regularized space-time adaptive processing using alternating direction method of multipliers

Generalized sidelobe cancellers for multidimensional separable arrays

High accuracy and low complexity adaptive Generalized Sidelobe Cancelers for colored noise scenarios

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Product

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Fast ℓ₁-regularized space-time adaptive processing using alternating direction method of multipliers

Fast ℓ₁-regularized space-time adaptive processing using alternating direction method of multipliers