In this paper, we discuss high-resolution target sensing through the exploitation of multi-frequency sparse arrayprocessing. By using simple design examples of a three-element sensorarray coupled with the sensing si...
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ISBN:
(纸本)9781728119465
In this paper, we discuss high-resolution target sensing through the exploitation of multi-frequency sparse arrayprocessing. By using simple design examples of a three-element sensorarray coupled with the sensing signals consisting of three well-designed frequencies, we provide insights to achieve a high number of consecutive lags, unique lags, and array aperture. Such multi-frequency sensorarrays with reduced number of sensors can be attractive in many applications to achieve effective sensing with a low cost.
Direction of arrival (DOA) estimation from array observations in a noisy environment is discussed. The source amplitudes are assumed to be correlated zero-mean complex Gaussian distributed with unknown covariance matr...
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ISBN:
(纸本)9781538647523
Direction of arrival (DOA) estimation from array observations in a noisy environment is discussed. The source amplitudes are assumed to be correlated zero-mean complex Gaussian distributed with unknown covariance matrix. The DOAs and covariance parameters of plane waves are estimated from multi-snapshot sensorarray data using sparse Bayesian learning (SBL). The performance of SBL is evaluated in terms of the fidelity of the reconstructed coherency matrix of the estimated plane waves.
The estimation of directions of arrival is formulated as the decomposition of a 3-way array into a sum of rank-one terms. However, a low-rank tensor approximation does not always exist. We propose an optimization tech...
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ISBN:
(纸本)9781479914814
The estimation of directions of arrival is formulated as the decomposition of a 3-way array into a sum of rank-one terms. However, a low-rank tensor approximation does not always exist. We propose an optimization technique based on differentiable angular constraints on the factors, ensuring the existence of the low-rank tensor decomposition. The efficiency of the proposed algorithm is demonstrated via numerical simulations, and compared to Cramer-Rao bounds.
The performance of most existing arrayprocessing algorithms relies heavily on the precise knowledge of array manifold, which is decided by individual sensor characteristics and array configuration. A major challenge ...
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ISBN:
(纸本)9781424422401
The performance of most existing arrayprocessing algorithms relies heavily on the precise knowledge of array manifold, which is decided by individual sensor characteristics and array configuration. A major challenge for self-calibration techniques is the increased computational burden due to additional perturbation parameters. In this contribution, a novel procedure for array self-calibration is presented. We apply the well known numerical method, the Space Alternating Generalized EM algorithm (SAGE), to simplify the multi-dimensional search procedure required for finding maximum likelihood (ML) estimates. Simulation shows that the proposed algorithm outperforms existing methods that are based on the small perturbation assumption. Furthermore, the proposed algorithm remain robust in critical scenarios including large sensor position errors and closely located signals.
In this paper, we develop and evaluate distributed implementations of source localization estimators from energy-based measurements obtained via an ad-hoc network of acoustic sensors. The distributed locally construct...
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ISBN:
(纸本)0780385454
In this paper, we develop and evaluate distributed implementations of source localization estimators from energy-based measurements obtained via an ad-hoc network of acoustic sensors. The distributed locally constructed algorithms that we present produce at each node a sequence of esLimates approximating a desired source localization algorithm. As our investigation reveals, the localization performance of these distributed algorithms depends on the type of desired localization algorithm, the network topology and the number of communication and fusion steps employed in these approximations.
Several methods have been developed which allow the estimation of the location of an existing source with considerable accuracy in the absence of multipaths. However, if, in addition to the Line-of-Sight (LOS) path, n...
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ISBN:
(纸本)9781479914814
Several methods have been developed which allow the estimation of the location of an existing source with considerable accuracy in the absence of multipaths. However, if, in addition to the Line-of-Sight (LOS) path, non-LOS (NLOS) paths are also present, then all existing localisation algorithms dramatically fail to estimate the location of the source. In this paper, a passive arrayprocessing algorithm is proposed, which, if used prior to a localisation approach, suppresses all the multipath contributions in the received signal except for that of the LOS path. The performance of the proposed algorithm is evaluated through computer simulation studies.
We consider a calibration problem, where we determine an unknown sensor location using the known track of a calibration target and a known reference sensor location. We cast the calibration problem as a sparse approxi...
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ISBN:
(纸本)9781424422401
We consider a calibration problem, where we determine an unknown sensor location using the known track of a calibration target and a known reference sensor location. We cast the calibration problem as a sparse approximation problem where the unknown sensor location is determined over a discrete spatial grid with respect to the reference sensor. To achieve the calibration objective, low dimensional random projections of the sensor data are passed to the reference sensor, which significantly reduces the inter-sensor communication bandwidth. The unknown sensor location is then determined by solving an l(1)-norm minimization problem (linear program). Field data results are provided to demonstrate the effectiveness of the approach.
This paper is devoted to under-determined linear mixtures of independent random variables (i.e. with more inputs than outputs). Blind identifiability of general under-determined mixtures is first discussed, and the ma...
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ISBN:
(纸本)0780385454
This paper is devoted to under-determined linear mixtures of independent random variables (i.e. with more inputs than outputs). Blind identifiability of general under-determined mixtures is first discussed, and the maximum number of sources is given, depending on the hypotheses assumed. Then an algorithm proposed by Taleb, essentially usable for 2-dimensional mixtures, is extended to the complex field. A procedure is proposed in order to avoid the enormous increase in complexity. Computer simulations demonstrate the ability of the algorithm to identify mixtures of N QPSK sources received on 1 or 2 sensors.
Many real world applications of target tracking and state estimation are non-linear filtering problems and can therefore not be solved by closed-form analytical solutions. In the recent past, tensor based approaches h...
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ISBN:
(纸本)9781538647523
Many real world applications of target tracking and state estimation are non-linear filtering problems and can therefore not be solved by closed-form analytical solutions. In the recent past, tensor based approaches have become increasingly popular due to very effective decomposition algorithms, which allow the representation of discretized, high-dimensional data in compressed form. In this paper, a solution of the prediction step for a Bayesian filter is proposed, where the probability density function (pdf) is approximated by a tensor in Hierarchical Tucker Decomposition. It is shown, that the computation of the predicted pdf is about five times faster than the previously proposed Canonical Polyadic Decomposition format.
This paper presents a new methodology for airborne wideband space-time adaptive processing (W-STAP) radar systems. In W-STAP, the wideband target signal is first decomposed into a series of narrowband signals. This is...
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ISBN:
(纸本)0780385454
This paper presents a new methodology for airborne wideband space-time adaptive processing (W-STAP) radar systems. In W-STAP, the wideband target signal is first decomposed into a series of narrowband signals. This is referred to as a sub-banding process. The different signals are then combined into a single reference sub-band (usually the center). STAP processing is then performed at this reference band. The combination process is done through a transformation from the different sub-bands into the reference one using the focusing approach [1]. In this method, non-sinaular transformation matrices are used. Compared to the conventional approach proposed in [2], the focusing technique provides greater Computational efficiency in wideband STAP systems.
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