This paper presents an operational system of digital transmission within the H.F. frequency range, aiming at a significant increase of the data transfer rate compared with the current standard. Therefore, an array pro...
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ISBN:
(纸本)0780374886
This paper presents an operational system of digital transmission within the H.F. frequency range, aiming at a significant increase of the data transfer rate compared with the current standard. Therefore, an array processing method performs with a set of four collocated sensors, the spatial responses of which are different one from each other. This diversity induces a well-conditioned problem of spatio-temporal equalization which tends to reduce the effect of multi-paths interference. The implemented method resorts to a classical L.M.S. algorithm involving training sequences. Several waveforms have been tested and the experimental results reach the expected goal as the data transfer rate increases up to 15 kbits/s in a bandwidth of 3 kHz.
In this paper the Multiple Signal Classification Method (MUSIC) is used to generate estimates of angles of arrival of multiple signals. The method involves determining the covariance matrix of the signals seen on the ...
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Circular features are commonly sought in digital image processing. SLIDE (Subspace based LIne DEtection) method proposed to estimate the center and the radius of a single circle by adapting array processing methods. R...
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Circular features are commonly sought in digital image processing. SLIDE (Subspace based LIne DEtection) method proposed to estimate the center and the radius of a single circle by adapting array processing methods. Recently, a virtual circular array was proposed to estimate the radii of several concentric circles. A difficulty arises when intersecting circles are expected. In this paper, for the first time, we propose to combine linear and circular antenna, to retrieve intersecting circles. We exemplify the proposed method on a set of hand-made and real-world images. copyright by EURASIP.
This paper proposes the generalized MUltiple SIgnal Classification (MUSIC)-like algorithm for robust MUSIC-like processing for underwater applications. The solution proposed in this paper is to generalize the noise co...
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This paper proposes the generalized MUltiple SIgnal Classification (MUSIC)-like algorithm for robust MUSIC-like processing for underwater applications. The solution proposed in this paper is to generalize the noise correlation assumption and include a noise correlation model in its problem formulation. By doing so, the proposed generalized MUSIC-like algorithm is able to provide robust MUSIC-like performances in any noise condition, so long as the noise correlation property of the environment is known partially. Results from simulations and real data processing show that our proposed algorithm is able to suppress spurious peaks caused by mismatched noise assumptions in standard MUSIClike algorithms. The bound of the controlling parameter denoted by beta for the proposed generalized MUSIC-like algorithm is also discussed in this paper. Performance study using Monte Carlo simulations shows that the proposed generalized MUSIC-like algorithm has the same resolving power as the MUSIC method but slightly poorer accuracy in direction-of-arrival (DOA) estimation. This paper also presents the results from real data processing by the generalized MUSIC-like algorithm and demonstrates better resolving power than the Capon and MUSIC algorithms used consistently in the experiment.
Multiple source signals impinging on an antenna array can be separated by time-frequency synthesis techniques. Averaging of the time-frequency distributions (TFDs) of the data across the array permits the spatial sign...
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Multiple source signals impinging on an antenna array can be separated by time-frequency synthesis techniques. Averaging of the time-frequency distributions (TFDs) of the data across the array permits the spatial signatures of sources to play a fundamental role in improving the synthesis performance. array averaging introduces a weighting function in the time-frequency domain that decreases the noise levels, reduces the interactions of the source signals, and mitigates the crossterms. This is achieved independent of the temporal characteristics of the source signals and without causing any smearing of the signal terms. The weighting function may take noninteger values, which are determined by the communication channel, the source positions, and their angular separations. Unlike the recently, devised blind source separation methods using spatial TFDs, the proposed method does not require whitening or retrieval of the source directional matrix. The paper evaluates the proposed method in terms of performance and computations relative to the existing source separation techniques based on quadratic TFDs.
Estimation of time-varying variances of signals for beamforming in sensor arrays is a challenging problem. Based on the assumption that the array manifold vector and the noise pseudo-coherence matrix are known a prior...
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Estimation of time-varying variances of signals for beamforming in sensor arrays is a challenging problem. Based on the assumption that the array manifold vector and the noise pseudo-coherence matrix are known a priori or are well estimated, we present in this paper two estimators for estimating the time-varying variances of the source signal of interest and the noise. These two estimators are then extended to deal with the following situations: 1) there are multiple candidates of the noise pseudo-coherence matrix or the noise pseudo-coherence matrix is a linear combination of some base pseudo-coherence matrices, and 2) the estimation variance is large and smoothing is needed. Simulations for speech enhancement applications are performed and the results show that the proposed estimators can well track the time-varying variances of both the speech and noise signals. It is also demonstrated that the optimal beamformer using the variance parameters estimated with the presented estimators outperforms the widely used traditional optimal beamformers in terms of improvement in both the signal-to-noise ratio (SNR) and the log-spectral distortion (LSD).
The canonical polyadic decomposition (CPD) plays an important role for signal separation in array processing. The CPD model requires arrays composed of several displaced but identical subarrays. Consequently, it is le...
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The canonical polyadic decomposition (CPD) plays an important role for signal separation in array processing. The CPD model requires arrays composed of several displaced but identical subarrays. Consequently, it is less appropriate for more complex array geometries. In this paper, we explain that coupled CPDallows a muchmore flexible modeling that can handle multiple shift-invariance structures, i.e., arrays that can be decomposed into multiple but not identical displaced subarrays. Both deterministic and generic identifiability conditions are presented. We also point out that, under mild conditions, the signal separation problem can in the exact case be solved by means of an eigenvalue decomposition. This is similar to ESPRIT, although the working conditions are much more relaxed. Borrowing tools from algebraic geometry, we derive generic uniqueness bounds for L-shaped, frame-shaped, and triangular-shaped arrays that come close to bounds that are necessary for uniqueness. Recognizing multiple shift invariance can be a bit of an art by itself. We explain that any centrosymmetric array processing problem can be interpreted in terms of a coupled CPD. In addition, we demonstrate that the coupled CPD model allows us to significantly relax the far-field assumption commonly used in CPD-based array processing.
We provide a cumulant-based blind beamforming method for recovery of statistically independent narrowband source signals in the presence of coherent (or perfectly correlated) multipath propagation, Our method is based...
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We provide a cumulant-based blind beamforming method for recovery of statistically independent narrowband source signals in the presence of coherent (or perfectly correlated) multipath propagation, Our method is based on the fact that for a blind beamformer, the presence of coherent multipaths is equivalent to the case of independent sources with a different steering matrix. Our approach is applicable to any array configuration having unknown response, Signal sources must have nonzero fourth-order cumulants, There is no need to estimate the directions of arrival. Our method maximizes signal-to-interference plus noise ratio (SINR), A comparable result does not exist using just second-order statistics.
In this paper, utilizing system theoretic concepts a sound, rigorous theory of array processing is established which leads to several new results. MUSIC, MIN-NORM, ESPRIT, and PISARENKO used for both spatial and tempo...
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In this paper, utilizing system theoretic concepts a sound, rigorous theory of array processing is established which leads to several new results. MUSIC, MIN-NORM, ESPRIT, and PISARENKO used for both spatial and temporal spectral decomposition of signals are well known techniques in array processing. In this work, a general approach generalizing them is presented. A theory for multipath case is provided for analysis and design of array structures without the assumption of linearity and equal spaceness which estimates the temporal frequency and the directions for coherent sources. Our approach is also developed to null signals in certain directions with certain frequencies, such as for multipath cancellation.
High performance signal parameter estimation from sensor array data requires knowledge of the spatial covariance matrix of the noise. This artifact can be overcome only by introducing alternative assumptions which ena...
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