Enhanced PUMA for direction-of-arrival estimation and its performance analysis

Cheng Qian; Lei Huang; Nicholas D. Sidiropoulos; Hing Cheung So

doi:10.1109/TSP.2016.2543206

Enhanced PUMA for direction-of-arrival estimation and its performance analysis

Cheng Qian, Lei Huang, Nicholas D. Sidiropoulos, Hing Cheung So

Department of Electrical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

70 Citations (Scopus)

Abstract

Direction-of-arrival (DOA) estimation is a problem of significance in many applications. In practice, due to the occurrence of coherent signals and/or when the number of available snapshots is small, it is a challenge to find DOAs accurately. This problem is revisited here through a new enhanced principal-singular-vector utilization for modal analysis (EPUMA) DOA estimation approach, which improves the threshold performance by first generating (P+K) DOA candidates for K sources where P ≥ K, and then judiciously selecting K of them. The asymptotic variance of EPUMA is theoretically derived, and numerical results are provided to validate the asymptotic analysis and illustrate the practical merits of EPUMA.

Original language	English
Article number	7435323
Pages (from-to)	4127-4137
Journal	IEEE Transactions on Signal Processing
Volume	64
Issue number	16
Online published	17 Mar 2016
DOIs	https://doi.org/10.1109/TSP.2016.2543206
Publication status	Published - 15 Aug 2016

Research Keywords

DOA estimation
linear prediction
small sample size
subspace method
weighted least squares

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title = "Enhanced PUMA for direction-of-arrival estimation and its performance analysis",

abstract = "Direction-of-arrival (DOA) estimation is a problem of significance in many applications. In practice, due to the occurrence of coherent signals and/or when the number of available snapshots is small, it is a challenge to find DOAs accurately. This problem is revisited here through a new enhanced principal-singular-vector utilization for modal analysis (EPUMA) DOA estimation approach, which improves the threshold performance by first generating (P+K) DOA candidates for K sources where P ≥ K, and then judiciously selecting K of them. The asymptotic variance of EPUMA is theoretically derived, and numerical results are provided to validate the asymptotic analysis and illustrate the practical merits of EPUMA.",

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AU - Qian, Cheng

AU - Huang, Lei

AU - Sidiropoulos, Nicholas D.

AU - So, Hing Cheung

PY - 2016/8/15

Y1 - 2016/8/15

N2 - Direction-of-arrival (DOA) estimation is a problem of significance in many applications. In practice, due to the occurrence of coherent signals and/or when the number of available snapshots is small, it is a challenge to find DOAs accurately. This problem is revisited here through a new enhanced principal-singular-vector utilization for modal analysis (EPUMA) DOA estimation approach, which improves the threshold performance by first generating (P+K) DOA candidates for K sources where P ≥ K, and then judiciously selecting K of them. The asymptotic variance of EPUMA is theoretically derived, and numerical results are provided to validate the asymptotic analysis and illustrate the practical merits of EPUMA.

AB - Direction-of-arrival (DOA) estimation is a problem of significance in many applications. In practice, due to the occurrence of coherent signals and/or when the number of available snapshots is small, it is a challenge to find DOAs accurately. This problem is revisited here through a new enhanced principal-singular-vector utilization for modal analysis (EPUMA) DOA estimation approach, which improves the threshold performance by first generating (P+K) DOA candidates for K sources where P ≥ K, and then judiciously selecting K of them. The asymptotic variance of EPUMA is theoretically derived, and numerical results are provided to validate the asymptotic analysis and illustrate the practical merits of EPUMA.

KW - DOA estimation

KW - linear prediction

KW - small sample size

KW - subspace method

KW - weighted least squares

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DO - 10.1109/TSP.2016.2543206

M3 - RGC 21 - Publication in refereed journal

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JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

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ER -

Enhanced PUMA for direction-of-arrival estimation and its performance analysis

Abstract

Research Keywords

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