独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE.

Jade performs `Source Separation in the following sense:

X is an n x T data matrix assumed modelled as X = A S + N where



o A is an unknown n x m matrix with full rank.

o S is a m x T data matrix (source signals) with the properties

　　a) for each t, the components of S(:,t) are statistically

　　 independent

　　b) for each p, the S(p,:) is the realization of a zero-mean

　　 `source signal .

　　c) At most one of these processes has a vanishing 4th-order

　　 cumulant.

o N is a n x T matrix. It is a realization of a spatially white

Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance

sigma. This is probably better than no modeling at a- Source separation of complex signals with JADE.

　Jade performs `Source Separation　in the following sense:

　　X is an n x T data matrix assumed modelled as X = A S+ N where

　

　o A is an unknown n x m matrix with full rank.

　o S is a m x T data matrix (source signals) with the properties

　　 　　a) for each t, the components of S(:,t) are statistically

　　 　　　 independent

　　　b) for each p, the S(p,:) is the realization of a zero-mean

　　　　 `source signal .

　　　c) At most one of these processes has a vanishing 4th-order

　　　　 cumulant.

　o　N is a n x T matrix. It is a realization of a spatially white

　　 Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance

　　 sigma.　This is probably better than no modeling at all...