mixing matrix estimation of underdetermined blind source separation based on data field and improved fcm clustering

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ID: 249613
2018
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Abstract
In modern electronic warfare, multiple input multiple output (MIMO) radar has become an important tool for electronic reconnaissance and intelligence transmission because of its anti-stealth, high resolution, low intercept and anti-destruction characteristics. As a common MIMO radar signal, discrete frequency coding waveform (DFCW) has a serious overlap of both time and frequency, so it cannot be directly used in the current radar signal separation problems. The existing fuzzy clustering algorithms have problems in initial value selection, low convergence rate and local extreme values which will lead to the low accuracy of the mixing matrix estimation. Consequently, a novel mixing matrix estimation algorithm based on data field and improved fuzzy C-means (FCM) clustering is proposed. First of all, the sparsity and linear clustering characteristics of the time–frequency domain MIMO radar signals are enhanced by using the single-source principal value of complex angular detection. Secondly, the data field uses the potential energy information to analyze the particle distribution, thus design a new clustering number selection scheme. Then the particle swarm optimization algorithm is introduced to improve the iterative clustering process of FCM, and finally get the estimated value of the mixing matrix. The simulation results show that the proposed algorithm improves both the estimation accuracy and the robustness of the mixing matrix.
Reference Key
guo2018symmetrymixing Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Qiang Guo;Chen Li;Guoqing Ruan
Journal journal of hospitality and tourism management
Year 2018
DOI
10.3390/sym10010021
URL
http://www.mdpi.com/2073-8994/10/1/21
Keywords
data field particle swarm optimizationmathematics

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