本文關(guān)鍵詞： 信號(hào)處理 獨(dú)立分量分析 源數(shù)估計(jì) 自然梯度法 單源點(diǎn)檢測法 子空間表示法　出處：《電光與控制》2017年08期 　論文類型：期刊論文

【摘要】：研究了源數(shù)估計(jì)對于獨(dú)立分量分析算法的影響。對于正定模型,當(dāng)估計(jì)源數(shù)少于真實(shí)源數(shù)時(shí),模型變?yōu)槌Ｐ?采用自然梯度法開展仿真實(shí)驗(yàn);當(dāng)估計(jì)源數(shù)多于真實(shí)源數(shù)時(shí),模型變?yōu)榍范Ｐ?采用基于時(shí)頻單源點(diǎn)檢測的混合矩陣估計(jì)算法和子空間表示信號(hào)恢復(fù)算法開展仿真實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果表明,在滿足一定信噪比的條件下,對于正定模型超定化問題,通常有數(shù)目等于估計(jì)源數(shù)的源信號(hào)能夠成功分離;對于正定模型欠定化問題,通常所有源信號(hào)都能正確分離,只是分離信號(hào)中出現(xiàn)了1個(gè)或多個(gè)源信號(hào)的拷貝,可以通過檢測分離信號(hào)的相關(guān)性,對拷貝信號(hào)進(jìn)行剔除或合并,對分離效果無影響。研究結(jié)論對于獨(dú)立分量分析算法的應(yīng)用具有一定參考價(jià)值。
[Abstract]:The influence of source number estimation on independent component analysis (ICA) algorithm is studied. For the positive definite model, when the estimated number of sources is less than the real number of sources, the model becomes overdetermined model, and the natural gradient method is used to carry out the simulation experiment. When the estimated number of sources is more than the real number of sources, the model becomes an underdetermined model. The hybrid matrix estimation algorithm based on time-frequency single source point detection and the subspace representation signal recovery algorithm are used to carry out simulation experiments. Under the condition of satisfying certain signal-to-noise ratio (SNR), there is usually a source signal with a number equal to the estimated number of sources which can be separated successfully for the problem of over-determination of the positive definite model. For the problem of unfixed positive definite model, usually all the source signals can be separated correctly, but there are one or more copies of the source signals in the separated signals, which can detect the correlation of the separation signals. Eliminating or merging the copy signal has no effect on the separation effect. The research results have some reference value for the application of the independent component analysis (ICA) algorithm.
【作者單位】： 電子信息系統(tǒng)復(fù)雜電磁環(huán)境效應(yīng)國家重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金(61372040) CEMEE國家重點(diǎn)實(shí)驗(yàn)室開放課題(CEMEE2015Z0302B)
【分類號(hào)】：TN911.7
【正文快照】： 0引言盲源分離(Blind Source Separation，

本文編號(hào)：1468596