文獻(xiàn)標(biāo)識(shí)碼： A
文章編號(hào)： 0258-7998(2012)02-0101-04
近年，盲信號(hào)分離BSS（Blind Source Separation）的研究已經(jīng)成為信號(hào)處理領(lǐng)域的一個(gè)研究熱點(diǎn)，涌現(xiàn)出許多盲分離的算法。盲信號(hào)分離是在源信號(hào)和傳輸信道參數(shù)未知的情況下，僅根據(jù)源信號(hào)的統(tǒng)計(jì)特性，從觀測(cè)信號(hào)中分離源信號(hào)的過(guò)程[1]。盲信號(hào)分離所研究的混疊模型主要分為瞬時(shí)混疊和卷積混疊兩類。瞬時(shí)盲分離已經(jīng)得到廣泛而成熟的研究，聯(lián)合塊（JBD）對(duì)角化是解決瞬時(shí)盲分離的有效方法[2-4]。然而，傳感器接收到的信號(hào)通常是源信號(hào)與多徑傳輸信道的卷積混疊信號(hào)，這使得卷積盲分離受到越來(lái)越多的關(guān)注[5-7]。
與瞬時(shí)混疊模型相比，卷積混疊信號(hào)模型及其求解更為復(fù)雜。在現(xiàn)有方法中，基于高階統(tǒng)計(jì)量的時(shí)域算法[8-9]是解卷積混疊盲信號(hào)分離問(wèn)題的一類直觀且有效的方法。作為時(shí)域算法，它不需要解決頻域算法[10-11]中所固有的又不得不解決的尺度模糊和排列模糊問(wèn)題；同時(shí)，對(duì)一組高階累積量矩陣同時(shí)進(jìn)行JBD又可以有效地抑制高斯噪聲的影響。鑒于這兩點(diǎn)，本文提出一種基于高階累積量的JBD時(shí)域算法，來(lái)解決卷積混疊盲信號(hào)分離問(wèn)題。
1 問(wèn)題描述
盲信號(hào)分離的目的是把通過(guò)一未知混合系統(tǒng)后的觀測(cè)信號(hào)分離開(kāi)來(lái)。在卷積混合情況下，假設(shè)源信號(hào)通過(guò)一個(gè)線性有限脈沖響應(yīng)FIR濾波器，也就是說(shuō)觀測(cè)信號(hào)是由它們的延遲所組成的線性組合，即：
用參考文獻(xiàn)[14]中所提到的自然梯度算法來(lái)分離卷積混合的源信號(hào),最后分離出來(lái)的信號(hào)波形如圖3所示。
從兩種算法分離出的信號(hào)波形圖中很難明顯看出其性能的差別，下面通過(guò)兩個(gè)性能指標(biāo)來(lái)客觀地分析一
陣。在此基礎(chǔ)上通過(guò)使代價(jià)函數(shù)最小化的方法來(lái)使累積量矩陣成為塊對(duì)角矩陣，進(jìn)而實(shí)現(xiàn)盲分離。計(jì)算機(jī)仿真結(jié)果表明，本文算法與自然梯度算法相比有分離精度高及分離速度快的特點(diǎn)。
參考文獻(xiàn)
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