Adaptive Array Principles

Hudson, J.E.

doi:10.1049/pbew011e

Cited by 456 publications

(150 citation statements)

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“…The n k are iid zero-mean complex circular Gaussian vectors with covariance matrix σ 2 I. The clutter vectors c k are iid complex circular Spherically Invariant Random Vectors (SIRV's) [13] with covariance matrix given by (6) and Gamma distributed squared textures whose shape parameter, which rules the impulsive nature of the clutter, is denoted by ν. The plots also assume ρ = 0.8, f c = 0.2, ν = 0.5, and CNR = 20 dB, which is a typical scenario of sea clutter.…”

Section: Simulated Datamentioning

confidence: 99%

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Knowledge-based recursive least squares techniques for heterogeneous clutter suppression

Maio

Farina

Foglia³

2007

IET Radar Sonar Navig.

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In this paper we deal with the design of Knowledge-Based adaptive algorithms for the cancellation of heterogeneous clutter. To this end we revisit the application of the Recursive Least Squares (RLS) technique for the rejection of unwanted clutter and devise modified RLS filtering procedure accounting for the spatial variation of the clutter power. Then we introduce the concept of Knowledge-Based RLS and explain how the a-priori knowledge about the radar operating environment can be adopted for improving the system performance. Finally we assess the benefits resulting from the use Knowledge-Based processing both on simulated and on measured clutter data collected by the McMaster IPIX radar in November 1993.

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Section: Simulated Datamentioning

confidence: 99%

“…To this end, we first revisit the Recursive Least Squares (RLS) algorithm [6], which permits a reduced complexity adaptive implementation of the optimum filter, designed under the assumption of homogeneous environment. Then we introduce a recursive procedures exploiting data normalizations.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based recursive least squares techniques for heterogeneous clutter suppression

Maio

Farina

Foglia³

2007

IET Radar Sonar Navig.

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“…The system performance is evaluated by means of computer simulation. When PF is less than or equal to 102, it is shown that the Gaussian receiver requires a sign~al to clutter ratio of [10][11][12][13][14][15][16][17][18][19][20] dB larger than that required by the LOD for the same values of PD and PF.…”

Section: Performance Of the Lod For The Multivariate Student-t Distrimentioning

confidence: 99%

“…Although this idea is similar to that of beam-space processing in [9,10,11] under the term of partially adaptive array processing, the work in [7,8] distinguishes itself from the previous study on beam-space processing in the following ways. References [7,8] are the first to point out that for the cases of the limited training-data size the use of localized adaptive processing is almost mandatory, and they have shown that localized adaptive processing can actually outperform fully adaptive processing in nonstationary and nonhomogeneous environments.…”

Section: The Joint-domain Localized Glr Algorithmmentioning

confidence: 99%

Signal Detection in Correlated Gaussian and Non-Gaussian Radar Clutter

Rangaswamy¹,

Chakravarthi²,

Weiner³

et al. 1993

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“…The technique applied to Radar or Wireless Communications Systems enhances performance [1][2][3][4][5]. Minimum-Variance-Distortionless-Response (MVDR) is a classical method used in adaptive beamforming of an antenna array [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Beamforming Algorithms for Cancellation of Multiple Interference Signals

Ong¹

2015

PIER M

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Abstract-This paper proposes a fast Minimum-Variance-Distortionless-Response (MVDR) beamforming algorithm for an antenna array for cancellation of multiple interference signals. The proposed algorithm uses Sample-Average Estimate (SAE) of the data covariance matrix and reduces its computational effort by applying the Matrix-Inversion-Lemma (MIL) to its covariance Matrix Inversion (MI) operation. The proposed algorithm is compared to two SAE-based algorithms: the Sample Matrix Inversion (SMI) algorithm that requires an MI operation and the Auxiliary Vector (AV) algorithm that does not need an MI operation. A non-SAE based algorithm using the Least Mean Square (LMS) method is also included for comparison. Simulation results show that the proposed algorithm converges slower than the SMI scheme but outperforms the AV and LMS schemes during the transient phase. Once convergence is achieved, the proposed algorithm converges to a better Mean Square Error than the rest of the algorithms evaluated.

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Adaptive Array Principles

Cited by 456 publications

References 0 publications

Knowledge-based recursive least squares techniques for heterogeneous clutter suppression

Knowledge-based recursive least squares techniques for heterogeneous clutter suppression

Signal Detection in Correlated Gaussian and Non-Gaussian Radar Clutter

Adaptive Beamforming Algorithms for Cancellation of Multiple Interference Signals

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