An algorithm for underdetermined mixing matrix estimation

Dong, Tianbao; Lei, Yingke; Yang, Jian

doi:10.1016/j.neucom.2012.09.018

Cited by 43 publications

(17 citation statements)

References 21 publications

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“…The accuracy of each cluster center was analyzed and calculated by substitution into Eq. (17). The specific performance parameters are shown in Fig.…”

Section: Experiments 2 and Analysis Of Resultsmentioning

confidence: 99%

“…These estimated mixing matrices were substituted into Eq. (17) to obtain the following: the NMSE forÂ 2 using only the DBSCAN algorithm was À 38.2753 dB, the NMSE for A 3 using the K-means only algorithm was À 34.1992 dB, the NMSE forÂ 4 using the K-Hough algorithm was À 40.9120 dB, and the NMSE forÂ 1 using the proposed algorithm was À 59.9163 dB. Thus, we can see that the accuracy of the estimated mixing matrix using the traditional K-means algorithm is relatively low, whereas that using the K-Hough algorithm with HT is higher but not sufficiently high, and it requires prior knowledge of the number of source signals.…”

Section: Experiments 2 and Analysis Of Resultsmentioning

confidence: 99%

“…Density-based spatial clustering of applications with noise (DBSCAN) [12][13][14] can overcome the drawbacks of the K-means clustering algorithm, but the parameter selection involved in this method is a challenging task. Further, single source point detection [15][16][17] has been proposed to address the problem of low signal sparsity [18][19][20][21], but it suffers from issues such as high complexity and conditions that are difficult to satisfy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Novel mixing matrix estimation approach in underdetermined blind source separation

Sun

Wen

et al. 2016

Neurocomputing

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“…The accuracy of each cluster center was analyzed and calculated by substitution into Eq. (17). The specific performance parameters are shown in Fig.…”

Section: Experiments 2 and Analysis Of Resultsmentioning

confidence: 99%

Section: Experiments 2 and Analysis Of Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel mixing matrix estimation approach in underdetermined blind source separation

Sun

Wen

et al. 2016

Neurocomputing

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“…The normalized mean square error is chosen as the evaluation criterion of the mixing matrix estimation [14,22,23], which can be expressed as:…”

Section: Evaluation Criteria Of Estimation Errormentioning

confidence: 99%

Mixing Matrix Estimation of Underdetermined Blind Source Separation Based on Data Field and Improved FCM Clustering

Guo

Ruan

2018

Symmetry

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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.

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“…We note that several algorithms in [9,[14][15][16] can obtain the mixing matrix successfully. In this paper, we assume that the mixing matrix has been estimated 55 accurately in the first stage of SCA-based method.…”

mentioning

confidence: 94%