Cramer-Rao bound analysis of wideband source localization and DOA estimation

Yip, L.; Chen, Joe C.; Hudson, Robert S.; Yao, Kung

doi:10.1117/12.453837

Cited by 15 publications

(24 citation statements)

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“…From (18), we see that is zero for all if and only if , which is equivalent to with . In [1], it is shown that is zero for uniform circular arrays. With Theorem 1, we extend this result to all isotropic arrays, and we also show that isotropy is a necessary and sufficient condition.…”

Section: A Planar Arraysmentioning

confidence: 99%

“…Since are independent samples of a zero-mean Gaussian process, the Fisher information matrix (FIM) on the the parameter vector is given by (see also [1], [3], and [4])…”

Section: A Planar Arraysmentioning

confidence: 99%

“…When the speed of propagation is unknown, the CRB on the DOA is given by the upper left entry of FIM given in (8) (see also [1])…”

Section: A Planar Arraysmentioning

confidence: 99%

“…In this letter, we generalize the result in [1] in three ways. First, we establish the entire class of array geometries for which the source location CRB is independent of .…”

mentioning

confidence: 99%

“…The effect of unknown speed of propagation on the DOA estimation performance of a sensor array is considered in [1], where the problem of localizing an unknown source using wideband measurements from an array of sensors is studied. The authors assume that is unknown and derive the Cramér-Rao bound (CRB) for the location of the source, which is the source DOA in the far-field case.…”

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confidence: 99%

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Source Localization With Isotropic Arrays

Oktel

Moses

2004

IEEE Signal Process. Lett.

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Abstract-We consider the effect of unknown signal propagation velocity on direction-of-arrival (DOA) estimation performance using an array of sensors. For general arrays, the DOA estimation variance, as measured by the Cramér-Rao bound (CRB), degrades when the propagation velocity is unknown. In this letter, we show that for both two-and three-dimensional arrays, the CRB on the DOA angle is independent of whether or not the propagation velocity is known if and only if the sensor locations satisfy the isotropy conditions given in earlier work. The results hold for both narrowband and wideband signals.

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Section: A Planar Arraysmentioning

confidence: 99%

“…Since are independent samples of a zero-mean Gaussian process, the Fisher information matrix (FIM) on the the parameter vector is given by (see also [1], [3], and [4])…”

Section: A Planar Arraysmentioning

confidence: 99%

“…When the speed of propagation is unknown, the CRB on the DOA is given by the upper left entry of FIM given in (8) (see also [1])…”

Section: A Planar Arraysmentioning

confidence: 99%

“…In this letter, we generalize the result in [1] in three ways. First, we establish the entire class of array geometries for which the source location CRB is independent of .…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Source Localization With Isotropic Arrays

Oktel

Moses

2004

IEEE Signal Process. Lett.

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Array Processing for Target DOA, Localization, and Classification Based on AML and SVM Algorithms in Sensor Networks

Yip

Comanor

Chen

et al. 2003

Information Processing in Sensor Networks

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Abstract. We propose to use the Approximate Maximum-Likelihood (AML) method to estimate the direction-of-arrival (DOA) of multiple targets from various spatially distributed sub-arrays, with each sub-array having multiple acoustical/seismic sensors. Localization of the targets can with possibly some ambiguity be obtained from the cross bearings of the sub-arrays. Spectra from the AML-DOA estimation of the target can be used for classification as well as possibly to resolve the ambiguity in the localization process. We use the Support Vector Machine (SVM) supervised learning method to perform the target classification based on the estimated target spectra. The SVM method extends in a robust manner to the nonseparable data case. In the learning phase, classifier hyperplanes are generated off-line via a primal-dual interior point method using the training data of each target spectra obtained from a single acoustical/seismic sensor. In the application phase, the classification process can be performed in real-time involving only a simple inner product of the classifier hyperplane with the AML-DOA estimated target spectra vector. Analysis based on Cramér-Rao bound (CRB) and simulated and measured data is used to illustrate the effectiveness of AML and SVM algorithms for wideband acoustical/seismic target DOA, localization, and classification.

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