English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, a generalized multivariate Student-t mixture model isdeveloped for classification and clustering of Low Probability of Interceptradar waveforms. A Low Probability of Intercept radar signal is characterizedby a pulse compression waveform which is either frequency-modulated orphase-modulated. The proposed model can classify and cluster differentmodulation types such as linear frequency modulation, non linear frequencymodulation, polyphase Barker, polyphase P1, P2, P3, P4, Frank and Zadoff codes.The classification method focuses on the introduction of a new priordistribution for the model hyper-parameters that gives us the possibility tohandle sensitivity of mixture models to initialization and to allow a lessrestrictive modeling of data. Inference is processed through a VariationalBayes method and a Bayesian treatment is adopted for model learning, supervisedclassification and clustering. Moreover, the novel prior distribution is not awell-known probability distribution and both deterministic and stochasticmethods are employed to estimate its expectations. Some numerical experimentsshow that the proposed method is less sensitive to initialization and providesmore accurate results than the previous state of the art mixture models.

A generalized multivariate Student-t mixture model for Bayesian classification and clustering of radar waveforms