Stratified multi-density spectral clustering using Gaussian mixture model

Yue, Guanli; Deng, Ansheng; Qu, Yanpeng; Cui, Hui; Wang, Xueying

doi:10.1016/j.ins.2023.03.067

Cited by 9 publications

(1 citation statement)

References 45 publications

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“….Then, based on the assumed complete data set, the so-called auxiliary Q function (E-step) is established, which is actually to calculate the conditional expectation of the likelihood function based on the complete data set about the hidden variable Y [8] . The formula is as follows:…”

Section: Em Algorithm Based On Mixed Gaussmentioning

confidence: 99%

Narrowband target tracking method based on gaussian mixture model

Zeng,

Ge,

Zhou

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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The method of trajectory distribution fusion based on Gaussian mixture model (GMM) is suitable for narrowband target tracking system. Aiming at the problems of inaccurate, ambiguous and narrowband tracking of wide-band tracking results, Aiming at the problems of inaccurate, ambiguous and narrowband tracking of Wide-band tracking results, an algorithm based on hybrid Gaussian model is proposed for automatic narrowband target tracking. An algorithm based on hybrid Gaussian model is proposed for automatic narrowband target tracking. Target azimuth distribution is regarded as mixed Gaussian model, Target Azimuth distribution is regarded as mixed Gaussian model, and the expectation maximization algorithm is used to estimate the parameters of the mixed Gaussian model. And the expectation maximization algorithm is used to estimate the parameters of the mixed Gaussian model. The Gaussian mixture model is used to cluster target azimuth. Target weighting method is used to fuse the target azimuth, and get clear and stable target tracking results.

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Section: Em Algorithm Based On Mixed Gaussmentioning

confidence: 99%