SIR: Signal to Interference Ratio

In JADE: Blind Source Separation Methods Based on Joint Diagonalization and Some BSS Performance Criteria

Signal to Interference Ratio

Description

Computes the signal to interference ratio between true and estimated signals

Usage

SIR(S, S.hat)

Arguments

S	Matrix or dataframe with the true numeric signals.
S.hat	Matrix or dataframe with the estimated numeric signals.

Details

The signal to interference ratio is measured in dB and values over 20 are thought to be good. It is scale and permutation invariant and can be seen as measuring the correlation between the matched true and estimated signals.

Value

The value of the signal to interference ratio.

Author(s)

Klaus Nordhausen

References

Eriksson, J., Karvanen, J. and Koivunen, V. (2000), Source distribution adaptive maximum likelihood estimation in ICA model, Proceedings of the second international workshop on independent component analysis and blind source separation (ICA 2000), 227–232.

See Also

amari.error, ComonGAP

Examples

S <- cbind(rt(1000, 4), rnorm(1000), runif(1000))
A <- matrix(rnorm(9), ncol = 3)
X <- S %*% t(A)

S.hat <- JADE(X, 3)$S
SIR(S, S.hat)

JADE documentation built on Sept. 18, 2023, 1:06 a.m.