Performance analysis of the optimum beamformer in the presence of correlated sources and its behavior under spatial smoothing

Reddy, V.U.; Paulraj, A.; Kailath, T.

doi:10.1109/tassp.1987.1165239

Cited by 165 publications

(77 citation statements)

References 14 publications

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“…Spatial smoothing is a technique used to alleviate the problems due to correlation where the array is divided into smaller sub arrays, and the average of the all the sub array covariance matrices are used to form a smoothed R matrix. In [23], it is shown that spatial smoothing progressively decorrelates the sources by diagonalizing the source covariance matrix. This decorrelation results in reduced signal cancellation and increased rejection of the coherent interference.…”

Section: Mvdr Algorithmsmentioning

confidence: 99%

Beamforming for Direction-of-Arrival (DOA) Estimation-A Survey

Krishnaveni¹,

Kesavamurthy²,

Aparna.³

2013

IJCA

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Direction-of-Arrival (DOA) estimation plays a vital role in many applications. Beamforming is the most prominent technique to estimate DOA. In this survey, a study of various beamforming techniques and algorithms to estimate the direction of arrival of a signal is made. An assessment on the background robust algorithms using Nyquist sampling rate and its Compressive sensing alternative is done. It is known that Bearing estimation algorithms obtain only a small number of direction of arrivals (DOAs) within the entire angle domain, when the sources are spatially sparse. Hence, it may be concluded that, the methods those specifically exploits this spatial sparsity property is advantageous. These methods use a very small number of measurements in the form of random projections of the sensor data along with one full waveform recording at one of the sensors.

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Section: Mvdr Algorithmsmentioning

confidence: 99%

Beamforming for Direction-of-Arrival (DOA) Estimation-A Survey

Krishnaveni¹,

Kesavamurthy²,

Aparna.³

2013

IJCA

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“…We see that correlation between desired signal and unwanted interferences can make the adaptive beamformers not only fail to form nulls in the direction of the coherent interferences, but also tend to cancel the desired signal in the output. For coping with the performance degradation due to the presence of coherent jammers, our approaches can be used in conjunction with spatial smoothing technique [1] or Toepliz approximation approach [10,6,7] to alleviate the coherent jammer effect. Numerical examples illustrate that the performance of the proposed beamformers is quite reliable, in particular, the two improved versions performs almost identically with the minimum mean square error (MMSE) beamformer, except at very low signal-to-noise ratio conditions.…”

Section: B Eigenspace-based MCMV Beamformermentioning

confidence: 99%

“…Conventional adaptive beamforming approaches, which assume uncorrelated signal sources, suffer from signal cancellation in the presence of coherent signals. To eliminate signal cancellation, possible solutions [1]- [4] include the use of averaging over either spatial or frequency domain to destroy the coherent components prior to beamforming. After de-correlating the coherent signals, the beamformer can then put nulls in their directions-of-arrival (DOAs).…”

Section: Introductionmentioning

confidence: 99%

Adaptive multiple-beamformers for reception of coherent signals with known directions in the presence of uncorrelated interferences

Zhang

et al. 2004

Signal Processing

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Abstract:Two novel adaptive multiple-beamformers for reception of coherent signals with known directions-of-arrival (DOAs) in the presence of uncorrelated interferences are proposed. The first approach is a two-step solution as follows: estimate the amplitudes of all the coherent signals using a subspace method and then construct the linearly constrained minimum variance beamformer from the generalized array manifold.The second approach applies multiple linear constraints determined from the DOAs of the coherent signals to develop a minimum variance beamformer, which can achieve efficient signal utilization. To cope with performance degradation due to sample covariance errors, DOA estimation errors and other array imperfections, the eigenstructure of the covariance matrix is exploited to improve the performance of the proposed approaches by constraining the weight vector in the signal subspace. Simulation results are presented which compare the performance of the proposed algorithms with that of the minimum mean square error approach.

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“…For near-field or multipath propagation, signal parameters have been extracted for the narrowband case, see e.g. [6], but the broadband approach is more difficult and often can only be made sufficiently robust to multipath effects without directly exploiting or extracting these [7].…”

Section: Introductionmentioning

confidence: 99%

Identification of Broadband Source-Array Responses from Sensor Second Order Statistics

Weiss

Goddard²,

Somasundaram

et al. 2017

2017 Sensor Signal Processing for Defence Conference (SSPD)

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Abstract-This paper addresses the identification of sourcesensor impulse responses from the measured space-time covariance matrix in the absence of any further side information about the source or the propagation environment. Using polynomial matrix decomposition techniques, the responses can be narrowed down to an indeterminacy of a common polynomial factor. If at least two different measurements for a source with constant power spectral density are available, this indeterminacy can be reduced to an ambiguity in the phase response of the sourcesensor paths.

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Performance analysis of the optimum beamformer in the presence of correlated sources and its behavior under spatial smoothing

Cited by 165 publications

References 14 publications

Beamforming for Direction-of-Arrival (DOA) Estimation-A Survey

Beamforming for Direction-of-Arrival (DOA) Estimation-A Survey

Adaptive multiple-beamformers for reception of coherent signals with known directions in the presence of uncorrelated interferences

Identification of Broadband Source-Array Responses from Sensor Second Order Statistics

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