Bearing estimation in the presence of unknown correlated noise

Farrier, D.R.; Jeffries, D.J.

doi:10.1109/icassp.1985.1168182

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…For the remainder of this work, we shall restrict our attention to two types of linear transformations: 1) premultiplication of r by a permutation matrix, and 2) complex conjugation. It is interesting to note that the latter transformation was first discussed in a related context by Farrier and Jeffries [21].…”

Section: B a Linear Transformation-based Approach To Covariance Diffmentioning

confidence: 99%

A transform-based covariance differencing approach for some classes of parameter estimation problems

Prasad

Williams

Mahalanabis

et al. 1988

IEEE Trans. Acoust., Speech, Signal Processing

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Abstract-In this paper, we present a method of parameter estimation for a class of problems where the desired signal is embedded in colored noise with unknown covariance. The new algorithm is a variation of the covariance differencing scheme proposed by Paulraj and Kailath. Unlike the previous method, however, the proposed algorithm does not require multiple estimates of the signal covariance matrix. Instead, it uses a priori knowledge of the structure of the noise covariance matrix to transform the array covariance matrix in a way that leaves its noise component unchanged while transforming the signal component in some appropriate manner. We can then eliminate the noise component by forming the difference of the original and transformed covariance matrices. This unique feature of the proposed method allows covariance differencing methods to be applied to a wider class of problems than was previously possible. To illustrate this, we apply the new covariance differencing algorithm to the problems of bearing estimation, resolution of overlapping echos, and transient response analysis. Simulation results are presented for each problem, and the new method's performance is compared to that of conventional methods for solving each respective problem. It is interesting to note that our algorithm eliminates the need for array rotation or translation which is often required for the conventional covariance differencing technique.

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Section: B a Linear Transformation-based Approach To Covariance Diffmentioning

confidence: 99%

A transform-based covariance differencing approach for some classes of parameter estimation problems

Prasad

Williams

Mahalanabis

et al. 1988

IEEE Trans. Acoust., Speech, Signal Processing

View full text Add to dashboard Cite

show abstract

“…In this study, a noise removal method is proposed. The received noise (see, e.g., [ 19 , 20 , 21 , 22 , 23 , 24 ]), which is from the 3D space, can be accurately modeled by adding the fields from a large number of uncorrelated sources (see, e.g., [ 24 , 25 , 26 , 27 ]). Theoretically, the noise covariance matrix is decomposed into symmetrical and asymmetrical components.…”

Section: Introductionmentioning

confidence: 99%

A Noise Removal Method for Uniform Circular Arrays in Complex Underwater Noise Environments with Low SNR

Xia

Wang

et al. 2017

Sensors

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Generally, many beamforming methods are derived under the assumption of white noise. In practice, the actual underwater ambient noise is complex. As a result, the noise removal capacity of the beamforming method may be deteriorated considerably. Furthermore, in underwater environment with extremely low signal-to-noise ratio (SNR), the performances of the beamforming method may be deteriorated. To tackle these problems, a noise removal method for uniform circular array (UCA) is proposed to remove the received noise and improve the SNR in complex noise environments with low SNR. First, the symmetrical noise sources are defined and the spatial correlation of the symmetrical noise sources is calculated. Then, based on the preceding results, the noise covariance matrix is decomposed into symmetrical and asymmetrical components. Analysis indicates that the symmetrical component only affect the real part of the noise covariance matrix. Consequently, the delay-and-sum (DAS) beamforming is performed by using the imaginary part of the covariance matrix to remove the symmetrical component. However, the noise removal method causes two problems. First, the proposed method produces a false target. Second, the proposed method would seriously suppress the signal when it is located in some directions. To solve the first problem, two methods to reconstruct the signal covariance matrix are presented: based on the estimation of signal variance and based on the constrained optimization algorithm. To solve the second problem, we can design the array configuration and select the suitable working frequency. Theoretical analysis and experimental results are included to demonstrate that the proposed methods are particularly effective in complex noise environments with low SNR. The proposed method can be extended to any array.

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Weak Signal Detection Method Based on Sector Spatial Pre-filtering

Zhang¹,

Yang

et al. 2008

2008 Congress on Image and Signal Processing

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Bearing estimation in the presence of unknown correlated noise

Cited by 6 publications

References 4 publications

A transform-based covariance differencing approach for some classes of parameter estimation problems

A transform-based covariance differencing approach for some classes of parameter estimation problems

A Noise Removal Method for Uniform Circular Arrays in Complex Underwater Noise Environments with Low SNR

Weak Signal Detection Method Based on Sector Spatial Pre-filtering

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