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Content Provider | IET Digital Library |
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Author | Liu, Yanyan Sun, Xiaoying Zhao, Shishun |
Abstract | We propose a source enumeration method via the generalised Bayesian information criterion (GBIC) based on a statistic for sphericity test in the white Gaussian and non-Gaussian noise under a large array with few samples. Instead of joint probability of observations or sample eigenvalue distribution, the proposed method is based on a statistic for testing the sphericity of a positive definite covariance matrix, to overcome the limitation of the Gaussian observations assumption. Under the white noise assumption, the covariance matrix of the noise subspace components of the observations is proportional to an identity matrix, and this identity structure can be tested by a statistic for sphericity test. The observations are decomposed into signal and noise subspace components under a presumptive number of sources. When the presumptive noise subspace components do not contain signals, the corresponding statistic for sphericity test will have a certain Gaussian distribution, and the number of sources can be estimated via the GBIC with the test statistic. Simulation results demonstrate that the proposed method provides high detection probability in both the Gaussian and the non-Gaussian noise, and performs better when the number of samples is less than the number of array sensors compared with other methods. |
Starting Page | 1333 |
Ending Page | 1339 |
Page Count | 7 |
ISSN | 17518784 |
Volume Number | 11 |
e-ISSN | 17518792 |
Issue Number | Issue 9, Sep (2017) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-rsn/11/9 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2016.0581 |
Journal | IET Radar, Sonar & Navigation |
Publisher Date | 2017-05-05 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Algebra Array Sensor Array Signal Processing AWGN Bayes Method Covariance Matrices Eigen Values And Eigen Function Eigenvalue Distribution Gaussian Distribution Gaussian Observations Assumption GBIC Generalised Bayesian Information Criterion Noise SubSpace Component NonGaussian Noise Positive Definite Covariance Matrix Probability Signal Processing And Detection Signal Processing Theory Signal Sampling Source Enumeration Method Sphericity Testing Statistical Testing Statistics White Gaussian Noise |
Content Type | Text |
Resource Type | Article |
Subject | Electrical and Electronic Engineering |
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