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Information Journal Paper

Title

Spatially Sparse Emitters Localization with QVBEM Algorithm

Pages

  105-109

Abstract

 In this paper, we study the estimation of the spatially sparse radio emitter locations via the proposed Quad-tree variational Bayesian expectation maximization (QVBEM) algorithm. Firstly, we assume that the emitters are approximately lie on a uniform grid points in the region under surveillance. The VBEM algorithm is applied and the points exceeding the threshold level are considered as potential targets. Then, the grids are refined around the potential targets via the Quad-tree algorithm and the process is iterated. It allows us to find the location of sparse emitters with much less computational complexity due to the use of fewer grid points. Simulation results show the superiority of the QVBEM to existing methods. The impact of threshold value on the performance of QVBEM is also studied.

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    • Cite

    APA: Copy

    Ramezani, Esmaeil. (2020). Spatially Sparse Emitters Localization with QVBEM Algorithm. ELECTRONIC INDUSTRIES, 11(1 ), 105-109. SID. https://sid.ir/paper/743628/en

    Vancouver: Copy

    Ramezani Esmaeil. Spatially Sparse Emitters Localization with QVBEM Algorithm. ELECTRONIC INDUSTRIES[Internet]. 2020;11(1 ):105-109. Available from: https://sid.ir/paper/743628/en

    IEEE: Copy

    Esmaeil Ramezani, “Spatially Sparse Emitters Localization with QVBEM Algorithm,” ELECTRONIC INDUSTRIES, vol. 11, no. 1 , pp. 105–109, 2020, [Online]. Available: https://sid.ir/paper/743628/en

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