A subspace-based direction finding algorithm using fractional lower order statistics

Liu, Tsung-Hsien; Mendel, Jerry M.

doi:10.1109/78.934131

Cited by 176 publications

(38 citation statements)

References 10 publications

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“…If the DOA estimation error for each source is less than 3 simultaneously, we call it a successful estimation. The probability of resolution is the ratio of the successful runs to the total Monte Carlo runs and the RMSE of those successful runs is defined by In this section, some simulations have been carried out to compare the performance of different methods including conventional MUSIC [4] , FLOM-MUSIC [10] , CRCO-MUSIC [15] , and the proposed SCC-MUSIC. The kernel size  is set as 3  = for CRCO-MUSIC, and the parameter p is set as 1.1 p = for FLOM-MUSIC according to the simulation results and the conclusions.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…To overcome the Alpha stable distribution noise, many DOA estimation algorithms based on the fractional lower-order statistics (FLOS) were proposed [8][9][10] . However, the algorithms have obtained certain estimation preformation, but have some limitations: (1) the characteristic exponent of noise must be estimated to ensure 1 p  or 02 p …”

Section: Introductionmentioning

confidence: 99%

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A Robust DOA Estimation Based on Sigmoid Transform in Alpha Stable Noise Environment

2018

MATEC Web Conf.

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To suppress alpha-stable noise and co-channel interference, this paper defines a novel cyclic correlation function, and proposes a new MUSIC algorithm based on sigmoid cyclic correlation function. Furthermore, the proposed algorithm (SCC-MUSIC) is applied to estimate direction of arrival (DOA) in alpha stable distribution noise. Simulation results demonstrate that the proposed SCC-MUSIC can get a better performance than several existing algorithms especially in the highly impulsive noise environments.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust DOA Estimation Based on Sigmoid Transform in Alpha Stable Noise Environment

2018

MATEC Web Conf.

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“…Boundedness is important for statistical signal processing in that statistical analysis cannot evaluate unbounded quantities to obtain reasonable conclusions. In open works, the effort by Tsakalides and Nikias [5], and Tsung-Hsien and Mendel [6] is remarkable. The classical MUSIC degrades because the covariance is sensitive to the impulsive noise, that is, the covariance is unbounded in alpha stable noise environment.…”

Section: Music Algorithm Based On Flocmentioning

confidence: 99%

“…In recent ten years, researchers began to focus on robust algorithms for estimation of DOA. Among these open documents, the robust covariation-based MUSIC (ROC-MUSIC) [5][6][7] is notable. The robust algorithm is less sensitive to impulsiveness and has high resolution, but it still requires eigen-value decomposition and involve mass computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Mobile communication array processing based on radial‐basis function neural network and fractional lower order statistics

Zha

2008

Trans Emerging Tel Tech

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Alpha stable distribution is better for modelling impulsive noises than Gaussian distribution in signal processing. This class of process has no close form of probability density function and finite second order moments. This paper addresses the direction of arrival estimation and beamforming problems of mobile communication system with linear antenna arrays in high impulsive noise environment. In order to reduce the computational complexity, the problems of DOA and beamforming are approached as a nonlinear mapping that can be modelled using a suitable radial-basis function neural network (RBFNN). This paper also proposes the application of a three-layer RBFNN to perform the DOA estimation and beamforming in presence of alpha stable distribution noises. The performance of the network is compared to that of the fractional lower order statistics based algorithms. Simulations show that the RBFNN is appropriate to approach the DOA estimation and beamforming. At the same time, the RBFNN substantially reduces the computation complexity.

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“…In open documents, ROC-MUSIC [4], FLOM-MUSIC [5], and MDD-MUSIC [6] are notable. Those algorithms are less sensitive to impulsive noise and have high resolution, but they still require eigenvalue decomposition and involve mass computational complexity.…”

Section: Introductionmentioning

confidence: 98%

Performance of direction of arrival estimation based on support vector regression in impulsive noise environment

Liu

Jiang

2009

2009 International Conference on Wireless Communications &Amp; Signal Processing

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In this paper, the problem of estimating the direction of arrival (DOA) in impulsive noise environments is considered. One possible way to model the impulsive noise is to introduce symmetric α -stable ( S S α ) distribution. Robust co variation based MUSIC (ROC-MUSIC) and Fractional lower moment based MUSIC (FLOM-MUSIC) can be used to estimate the DOA under these conditions. But those methods require eigenvalue decomposition and involve mass computational complexity. In order to reduce the computational burden, the problem of DOA of can be approximated as a nonlinear mapping by means of support vector regression. This paper discusses the application of SVR-based DOA in presence of impulsive noise. Moreover, performance of this new algorithm is analyzed by comparison. Computer simulation results verify the correctness and effectiveness of the proposed method.

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A subspace-based direction finding algorithm using fractional lower order statistics

Cited by 176 publications

References 10 publications

A Robust DOA Estimation Based on Sigmoid Transform in Alpha Stable Noise Environment

A Robust DOA Estimation Based on Sigmoid Transform in Alpha Stable Noise Environment

Mobile communication array processing based on radial‐basis function neural network and fractional lower order statistics

Performance of direction of arrival estimation based on support vector regression in impulsive noise environment

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