The maximal number of sources that can be uniquely estimated is the nearest integer less than or equal to N2/2.
Thus, provided R ≥ M, A, B, and C can be uniquely estimated up to scaling and column permutation by transmission of only two identical training symbols.
The necessary and sufficient conditions for ambiguity-free blind estimation can be determined using identifiability analysis, which determines whether a parameter can be uniquely estimated without any ambiguity [19, 23, 28 30].
Specifically, Oliveri and von Davier ([2011], [2014]) have provided empirical evidence that allowing a subset of item parameters to be uniquely estimated offers one way to improve model-to-data fit and reduces problems with comparability across heterogeneous populations and associated parameter estimate bias.
The biophysical parameters were uniquely estimated as αe = αx = α≈0, βe≈0.113, and βx≈0.887.
In this way, the corresponding parameters (p l +1, ⋯, p l + k ) can be uniquely estimated.
A model is structurally identifiable if its parameters can be uniquely estimated by fitting the model to observed data.
This larger problem also includes identifiability analysis, which determines whether the unknown parameter values can be uniquely estimated [ 40, 41].
An aim of identifiability analysis is to determine if the parameters of a model are identifiable or not, i.e. whether its parameters can be uniquely estimated.
If all state variables are measurable, according to Eq. (4), this subset of parameters can be uniquely estimated based on the k data sets.
