Efficient array design algorithm for wide-band application of the MUltiple SIgnal Classification algorithm

Self Cite

This paper considers wide-band DOA estimation using the spatial resampling method for coherent averaging. In this paper a rough estimate of the resampling error distribution is obtained. Careful examination of this distribution yields an improved resampling formula suitable for, but not limited to, arrays with finite sensors. It is shown that the proposed formula yields resampling error much less than that of conventional resampling. Moreover it is shown how the resampling error can be more reduced by proper choice of the focusing frequency. Based on the this resampling formula a perfect focusing scheme is then proposed and its performance is evaluated and compared to other wide-band methods through simulations. Simulations showed quite satisfactory and robust performance of the proposed scheme. It was shown that it succeeds in situations where all considered wideband methods fail. In addition it is bias-free and can be implemented quite efficiently. Hence it is quite suitable for reliable real-time DOA estimation in reverberant environments.

Section: Performance Of the Proposed Pfisr Methodsmentioning

confidence: 99%

Section: Newmentioning

confidence: 99%

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Improved finite samples resampling for unbiased wide-band direction of arrival estimation

Desoki

Takada

Hagiwara

2009

Self Cite

“…Direction-of-arrival (DOA) estimation is an area of active interest with a broad range of applications in sonar, radar, navigation and other fields [1,2]. In underwater acoustic signal processing, traditional approaches for DOA estimation usually make utilize of a spatially distributed array of pressure sensors, while over-looking the three components of particle velocity [3].…”

Section: Introductionmentioning

confidence: 99%

Vector-sensor array direction-of-arrival estimation exploiting spatial time-frequency structure based on joint approximate diagonalization

Song

Yang

Wang

2019

By making use of the extra particle velocity information, an array of vector sensors can achieve better Direction-of-arrival (DOA) estimation performance than a conventional array of pressure sensors. However, it is noted that most of the previous work on DOA estimation with vectorsensor array uses only the time-space statistical information available on the array signals and does not exploit the difference in the time-frequency signatures of the sources. In this paper, we develop a new approach which exploits the inherent time-frequency-space characteristics of the underlying vectorsensor array signal to achieve better DOA estimation performance even in a noisy and coherent environment with few snapshots. It turns out that our approach is based on the spatial time-frequency distributions (STFD) information and can efficiently combine all of the relevant STFD points by the joint approximate diagonalization approach, such as Jacobi rotation, to reduce the effect of noise and achieve the desired angular resolution. Computer simulations with several frequently encountered scenarios, such as multiple closely spaced coherent sources, indicate the superior DOA estimation resolution of our proposed approach as compared with existing techniques. In addition, from a statistical point of view, the performance of our proposed approach is investigated more closely by considering the root mean square error (RMSE) respectively versus SNRs, snapshots, or number of sensors and its excellent performance for higher DOA estimation accuracy is demonstrated.

“…In array signal processing, direction-of-arrival (DOA) estimation is a problem of interest in a variety of applications, including sonar, radar, navigation, and other fields [1][2][3]. During the last half century, several standard array apertures have been designed.…”

Section: Introductionmentioning

confidence: 99%

Circular array direction-of-arrival estimation in modal space using sparsity constraint

Song¹,

Qin

Yang

et al. 2018

A uniform circular array (UCA) can provide 360 azimuthal coverage and can be steered in any direction in two-dimensional (2-D) space without changing the shape of the pattern. These attractive features led to the rapid development of direction-of-arrival (DOA) estimation techniques using a UCA. However, most of the previous work on DOA estimation based on a UCA only utilized the time-space statistical information available from the array signals and did not exploit the inherent sparsity of the underlying signal in space domains. In this paper, we develop a new circular array DOA estimation approach that can achieve spatial sparsity, and thus improve spatial resolution, by imposing penalties based on the l 1-norm. Our approach differs from most other circular array DOA estimation methods not only in its recognition of the concept of phase modes but also in how it imposes the spatial sparsity constraint on the impinging signal wave fields to achieve better DOA estimation performance. It turns out that our approach, in essence, applies the compressive sensing technique to the modal array signal processing and does not need to reconstruct the correlation matrix or its inverse, so can work well when the sources are coherent. Computer simulations with several frequently encountered scenarios, such as a single source and two closely spaced coherent sources, indicate the superior DOA estimation resolution of our proposed approach as compared with existing techniques. In addition, from a statistical viewpoint, the performance of our proposed approach is investigated more closely by considering the root-mean-square error (RMSE) versus the signal-to-noise ratio (SNR), number of snapshots, or number of sensors, and its excellent DOA estimation accuracy is demonstrated.