Detection of Distributed Sources Using Sensor Arrays

Jin, Yufeng; Friedlander, B.

doi:10.1109/tsp.2004.827196

Cited by 27 publications

(19 citation statements)

References 19 publications

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“…degrees (or equivalently BW), and is consistent with the result in [12]. Choosing the subarray size Ç R will lead to a significant resolution loss for the nocoherent subarray detector as is shown in Fig.…”

Section: C2 Sinr Gain Without Training Datasupporting

confidence: 83%

“…The subspace manifold can be obtained in this case as follows (see also [12]). We start with the signal source.…”

Section: A Array Signal Modelmentioning

confidence: 99%

“…Noticing equation (12), this question can be easily answered from the reduced rank detection theory standpoint. The block diagram of the processing scheme is depicted in figure 2.…”

Section: B the Coherent Detectormentioning

confidence: 99%

“…Employing the matrix inversion lemma, it is then straight-forward to write the log-likelihood function as follows [12]:…”

mentioning

confidence: 99%

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Reduced-rank adaptive detection of distributed sources using subarrays

Jin

Friedlander

2005

IEEE Trans. Signal Process.

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We introduce a framework for exploring array detection problems in a reduced dimensional space. This involves calculating a structured subarray transformation matrix for the detection of a distributed signal using large aperture linear arrays. We study the performance of the adaptive subarray detector and evaluate its potential improvement in detection performance compared with the full array detector with finite data samples. One would expect that processing on subarrays may result in performance loss in that smaller number of degrees of freedom is utilized.However it also leads to a better estimation accuracy for the interference and noise covariance matrix with finite data samples, which will yield some gain in performance. By studying the subarray detector for general linear arrays, we identify this gain under various scenarios. We show that when the number of samples is small, the subarray detectors have a significant gain over the full array detector. In addition, the subarray processing can also be successfully applied to the problem of detecting moving sources in an underwater acoustic scenario. We validate our results by computer simulations.

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Section: C2 Sinr Gain Without Training Datasupporting

confidence: 83%

“…The subspace manifold can be obtained in this case as follows (see also [12]). We start with the signal source.…”

Section: A Array Signal Modelmentioning

confidence: 99%

“…Noticing equation (12), this question can be easily answered from the reduced rank detection theory standpoint. The block diagram of the processing scheme is depicted in figure 2.…”

Section: B the Coherent Detectormentioning

confidence: 99%

“…Employing the matrix inversion lemma, it is then straight-forward to write the log-likelihood function as follows [12]:…”

mentioning

confidence: 99%

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Reduced-rank adaptive detection of distributed sources using subarrays

Jin

Friedlander

2005

IEEE Trans. Signal Process.

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“…As one example, these conditions are observed in the current GSM and 3G networks where the base station is usually placed on the building roofs. As in [14,5,15,7], we suppose that the base station antenna-elements are isotropic and that the same mean AoA and AS are seen at all antenna-elements of the base station.…”

Section: Notations and Data Modelmentioning

confidence: 99%

A new low-complexity angular spread estimator in the presence of line-of-sight with angular distribution selection

Bousnina

Stéphenne

Affes

et al. 2011

EURASIP J. Adv. Signal Process.

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This article treats the problem of angular spread (AS) estimation at a base station of a macro-cellular system when a line-of-sight (LOS) is potentially present. The new low-complexity AS estimator first estimates the LOS component with a moment-based K-factor estimator. Then, it uses a look-up table (LUT) approach to estimate the mean angle of arrival (AoA) and AS. Provided that the antenna geometry allows it, the new algorithm can also benefit from a new procedure that selects the angular distribution of the received signal from a set of possible candidates. For this purpose, a nonlinear antenna configuration is required. When the angular distribution is known, any antenna structure could be used a priori; hence, we opt in this case for the simple uniform linear array (ULA). We also compare the new estimator with other low-complexity estimators, first with Spread Root-MUSIC, after we extend its applicability to nonlinear antenna array structures, then, with a recently proposed two-stage algorithm. The new AS estimator is shown, via simulations, to exhibit lower estimation error for the mean AoA and AS estimation.

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Adaptive Signal Design For MIMO Radars

Stoica

2008

MIMO Radar Signal Processing

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Detection of Distributed Sources Using Sensor Arrays

Cited by 27 publications

References 19 publications

Reduced-rank adaptive detection of distributed sources using subarrays

Reduced-rank adaptive detection of distributed sources using subarrays

A new low-complexity angular spread estimator in the presence of line-of-sight with angular distribution selection

Adaptive Signal Design For MIMO Radars

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