Self-calibration algorithms estimate both source directions-of-arrival (DOAs) and perturbed array response vector parameters, such as sensor locations. Calibration errors are usually assumed to be small and a first or...
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ISBN:
(纸本)0780363396
Self-calibration algorithms estimate both source directions-of-arrival (DOAs) and perturbed array response vector parameters, such as sensor locations. Calibration errors are usually assumed to be small and a first order approximation to the perturbed array response vector is often used to simplify the estimation procedure. ln this paper, we improve on a previously presented technique that eliminates the small error assumption. The improved technique has better performance for closely spaced sources for both small and large calibration errors.
This paper addresses the localization of an unknown number of acoustic sources in an enclosure. We extend a well established algorithm for localization of acoustic sources, which is based on the Expectation Maximizati...
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ISBN:
(纸本)9781538647523
This paper addresses the localization of an unknown number of acoustic sources in an enclosure. We extend a well established algorithm for localization of acoustic sources, which is based on the Expectation Maximization (EM) algorithm for clustering of phase differences by a Gaussian mixture model. Supporting a more appropriate probabilistic model for spherical data such as direction of arrival or phase differences, the von Mises distribution is used to derive a localization algorithm for multiple simultaneously active sources. Experiments with simulated room impulse responses confirm the superiority of the proposed algorithm to the existing method in terms of localization performance.
The ESPRIT method is a classical method for one-dimensional harmonic retrieval. During the past two decades it has become apparent that several applications in signalprocessing correspond to the less studied Multidim...
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ISBN:
(纸本)9781479914814
The ESPRIT method is a classical method for one-dimensional harmonic retrieval. During the past two decades it has become apparent that several applications in signalprocessing correspond to the less studied Multidimensional Harmonic Retrieval (MHR) problem. In order to accommodate this demand, we propose an extension of ESPRIT to MHR based on the coupled canonical polyadic decomposition. This leads to a dedicated uniqueness condition and an algebraic framework for MHR.
In this paper, an alternative Target Density Function (TDF) is proposed to image the radar targets in a dense target environment. It is obtained by considering a novel range and scanning angle plane different from the...
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ISBN:
(纸本)1424403081
In this paper, an alternative Target Density Function (TDF) is proposed to image the radar targets in a dense target environment. It is obtained by considering a novel range and scanning angle plane different from the conventional methods. An alternative method is briefly proposed for smoothing the target density function by taking advantage of Walsh functions. Although the imaging is obtained via the phased array radars, the problem associated with beamforming in linear phased array radar system is bypassed in this new algorithm.
Tracking signals in dynamic environments presents difficulties in both analysis and implementation. In this work, we expand on a class of subspace tracking algorithms which utilize the Grassmann manifold - the set of ...
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ISBN:
(纸本)9798350344820;9798350344813
Tracking signals in dynamic environments presents difficulties in both analysis and implementation. In this work, we expand on a class of subspace tracking algorithms which utilize the Grassmann manifold - the set of linear subspaces of a high-dimensional vector space. We design regularized least squares algorithms based on common manifold operations and intuitive dynamical models. We demonstrate the efficacy of the approach for a narrowband beamforming scenario, where the dynamics of multiple signals of interest are captured by motion on the Grassmannian.
In this paper, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-tointerference-plus-noise ratio (SINR). The ...
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ISBN:
(纸本)9781665406338
In this paper, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-tointerference-plus-noise ratio (SINR). The proposed method uses the alternating direction method of multipliers (ADMM), and admits closed-form solutions at each ADMM iteration. Numerical results exhibit excellent performance of the proposed method, which is comparable to that of the exhaustive search approach.
In many signalprocessing applications of linear algebra tools, the signal part of a postulated model lies in a so-called signal sub-space, while the parameters of interest are in one-to-one correspondence with a cert...
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ISBN:
(纸本)0780385454
In many signalprocessing applications of linear algebra tools, the signal part of a postulated model lies in a so-called signal sub-space, while the parameters of interest are in one-to-one correspondence with a certain basis of this subspace. The signal subspace can often be reliably estimated from measured data, but the particular basis of interest cannot be identified without additional problem-specific structure. This is a manifestation of rotational indeterminacy, i.e., non-Uniqueness of low-rank matrix decomposition. The situation is very different for three- or higher-way arrays, i.e., arrays indexed by three or more independent variables, for which low-rank decomposition is unique under mild conditions. This has fundamental implications for DSP problems which deal with such data. This paper provides a brief tour of the basic elements of this theory, along with many examples of application in problems of current interest in the signalprocessing community.
In this paper, the performance of a subspace beamformer, namely the multiple signal classification algorithm (MUSIC), is scrutinized in the presence of sensor position errors. Based on a perturbation model, a relation...
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ISBN:
(纸本)0780375513
In this paper, the performance of a subspace beamformer, namely the multiple signal classification algorithm (MUSIC), is scrutinized in the presence of sensor position errors. Based on a perturbation model, a relationship between the array autocorrelation matrix and the source autocorrelation matrix is established. It is shown that under certain assumptions on the source signals, the Gaussian sensor perturbation errors can be modelled as additive white Gaussian noise (AWGN) for an array where sensor positions are known perfectly. This correspondence can be used to equate position errors to an equivalent signal-to-noise ratio (SNR) for AWGN in performance evaluation. Finally, Cramer-Rao bound for the position perturbations that can be computed using the Cramer-Rao bound relations for the additive Gaussian noise case at high SNR's.
Target parameter estimation from noisy and quantized received signals is of paramount importance in radar applications. In this paper, we propose a novel method to estimate the unknown target parameters via one-bit sa...
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ISBN:
(纸本)9781538647523
Target parameter estimation from noisy and quantized received signals is of paramount importance in radar applications. In this paper, we propose a novel method to estimate the unknown target parameters via one-bit sampling, where the samples are produced by comparing the received signal with a time-varying threshold. The proposed approach utilizes a weighted least-squares criterion to establish a connection to previous results in radar target estimation and signalprocessing. Several numerical examples are provided to demonstrate the effectiveness of the proposed approach.
The partial adaptive concentric ring array (CRA) has been successfully applied to 3-D beamforming because of its flexibility, faster tracking ability and reduced computation with respect to the fully adaptive CRA. In ...
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ISBN:
(纸本)9781424422401
The partial adaptive concentric ring array (CRA) has been successfully applied to 3-D beamforming because of its flexibility, faster tracking ability and reduced computation with respect to the fully adaptive CRA. In some cases, prior knowledge regarding some interferences is available so that better beamformers can be designed. The previous method that exploits prior knowledge by using a fixed penalty factor could not guarantee in maintaining a low residual interference and noise level. We propose in this paper an adaptive beamformer that automatically seeks out the optimum penalty factor to achieve the best performance. The proposed beamformer outperforms the previous design in maintaining a higher output signal to interference and noise ratio, even after the characteristics of the interferences have changed. The performance of the proposed beamformer is evaluated through simulations.
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