A comparison of robust adaptive beamforming algorithms

Ward, James E.; Cox, Henry; Kogon, S.M.

doi:10.1109/acssc.2003.1292206

Cited by 20 publications

(12 citation statements)

References 4 publications

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“…The bottom right-hand side of (14) is only an approximation of the true f ðlÞ in (8) which holds under the hypothesis (10). The expression in (14) is likely not to be very accurate for large values of l as the ratios…”

Section: Optimisation For a Given Steering Vector Errormentioning

confidence: 99%

“…It should be pointed out that it is in this type of situation that the use of a robust beamformer based on diagonal loading is advisable. Indeed, as discussed in [8,10], robust adaptive beamformers using diagonal loading are suitable when one wishes to recover a weak signal in the presence of strong interferences located outside the main beam. In contrast, in the case of main beam jamming, it would perform rather poorly (mainly due to a lack of resolution compared to the fully adaptive beamformer without loading) and therefore its use would not be recommended anyway.…”

Section: Optimisation For a Given Steering Vector Errormentioning

confidence: 99%

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Steering vector errors and diagonal loading

Vincent¹,

Besson²

2004

IEE Proc., Radar Sonar Navig.

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Diagonal loading is one of the most widely used and effective methods to improve robustness of adaptive beamformers. The authors consider its application to the case of steering vector errors, i.e. when there exists a mismatch between the actual steering vector of interest and the presumed one. More precisely, the problem addressed is that of optimally selecting the loading level with a view to maximising the signal-to-interference-plus-noise ratio in the presence of steering vector errors. First, an expression is derived for the optimal loading for a given steering vector error and it is shown that this loading is negative. Next, random steering errors are considered and the optimal loading is averaged with respect to the probability density function of the steering vector errors, yielding a very simple expression for the average optimal loading. Numerical simulations attest to the validity of the analysis and show that diagonal loading with the derived optimal loading factor provides a performance close to optimum.

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Section: Optimisation For a Given Steering Vector Errormentioning

confidence: 99%

Section: Optimisation For a Given Steering Vector Errormentioning

confidence: 99%

Steering vector errors and diagonal loading

Vincent¹,

Besson²

2004

IEE Proc., Radar Sonar Navig.

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“…Another principle was suggested by Stoica et al (2003), which applies an NC on the di¤erence between the outcoming weight vector and the given steering vector (Robust Capon Beamformer (RCB) method). It has been reported that the RCB method is stable against the same kinds of errors as is the NC method Ward et al 2003).…”

Section: Introductionmentioning

confidence: 98%

Multistatic Radar Observation of a Fine-Scale Wind Field with a Coupling-Compensated Adaptive Array Technique

Nishimura

Harada

Sato

2010

Journal of the Meteorological Society of Japan

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As part of the Coupling Processes in the Equatorial Atmosphere (CPEA-II) Campaign, multistatic radar observations of local wind field were conducted using the Equatorial Atmosphere Radar and two auxiliary receiver arrays in West Sumatra, Indonesia, in December 2005. For obtaining velocity estimates from atmospheric echoes received by the two small arrays that have a high sidelobe level due to their small e¤ective aperture, an adaptive clutter rejection process is required. Because of the existence of electromagnetic coupling between antennas and the ground, the relative phase, in terms of an echo received by separate antennas, cannot be accurately predicted only with given positions of antennas and the target volume. The phase error leads to degradation of desired atmospheric echoes in the output of the adaptive clutter rejection process. In order to compensate for the mutual coupling e¤ect, we proposed a method to estimate the phase error corresponding to several directions in which atmospheric echoes were observed with particularly high signal-to-noise ratio (SNR). The estimates were interpolated at every necessary directional point using linear fit, thereby resulting in a successful removal of ground clutters without a serious loss of SNR. With this method, we have obtained a partial data set of the wind field. The wind field in the lower troposphere shows small-scale fluctuations on a horizontal scale of @500 m. We also present an attempt to interpret the fluctuation in a composite of multiple plane waves, employing a nonorthogonal decomposition method using a generalized inverse.

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“…A commonly used technique that addresses mismatch is to apply diagonally loading to the covariance matrix estimate [2] and recently in [6] is also known as a White Noise Constraint (WNC).…”

Section: Beamforming Algorithms For Processing Sbcx Experimental mentioning

confidence: 99%

Computationally efficient beamforming on real experimental data

Marino

Chau

Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.

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We investigate processing Santa Barbara Channel Experiment (SBCX) data, using Beamforming algorithms that utilize a computationally efficient covariance matrix (CECM) estimate. The algorithms using the CECM are the Bartlett beamformer (CBF) algorithm, the Minimum Variance Distortionless Response (MVDR) and a white noise gain constrained MVDR (WNGC-MVDR) and a Multiple Signal Characterization algorithm (MUSIC). The processing shows that for all the beamforming algorithms using the CECM in lieu of the sample covariance matrix (SCM) exhibited comparable performance, often overlapping at a reduced computational cost of greater then 20%.

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A comparison of robust adaptive beamforming algorithms

Cited by 20 publications

References 4 publications

Steering vector errors and diagonal loading

Steering vector errors and diagonal loading

Multistatic Radar Observation of a Fine-Scale Wind Field with a Coupling-Compensated Adaptive Array Technique

Computationally efficient beamforming on real experimental data

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