This paper presents a method for blind identification of a system whose transfer matrix is non-invertible at infinity, based on independent component analysis. In the proposed scheme, the transfer matrix to be identified is pre-multiplied by an appropriate polynomial matrix, named interactor, in order to compensate the row relative degrees and obtain a biproper system. It is then pre-multiplied by a demixing matrix via an existing approximate method. Both of these matrices are estimated blindly, i.e. with the input signals being unknown. The identified system is thus obtained as the inverse of the multiplication of these matrices.

In this paper, we propose a blind adaptive channel identification and equalization algorithm with phase offset compensation for single-input multiple-output (SIMO) channel. It is based on the one-step forward multichannel linear prediction error method and can be implemented by an RLS algorithm. Phase offset problem is inherent part of any second-order statistics-based blind identification and equalization. To solve this problem, we use a blind adaptive algorithm called the constant modulus derotator (CMD) algorithm based on constant modulus algorithm (CMA). Moreover, unlike many known subspace (SS) methods or cross relation (CR) methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch.

This paper considers a link of two problems; multichannel blind deconvolution and multichannel blind identification of linear time-invariant dynamic systems. To solve these problems, cumulant maximization has been proposed for blind deconvolution, while cumulant matching has been utilized for blind identification. They have been independently developed. In this paper, a cumulant maximization criterion for multichannel blind deconvolution is shown to be equivalent to a least-squares cumulant matching criterion after multichannel prewhitening of channel outputs. This equivalence provides us with a new link between a cumulant maximization criterion for blind deconvolution and a cumulant matching criterion for blind identification.

An algorithm for blind identification of multichannel (single-input and multiple-output) FIR systems is proposed. The proposed algorithm is based on subspace approach to blind identification, which requires so-called noise space spanned by some eigenvectors of correlation matrices of observations. It is shown that a subspace of the noise space can be obtained by one-step scalar-valued linear prediction and then the subspace is sufficient for blind identification. To acquire the subspace, the proposed algorithm utilizes one-step scalar-valued linear prediction in place of a singular- (or eigen-) value decomposition and hence it is computationally efficient. Computer simulations are presented to compare the proposed algorithm with the original one.

This paper presents an approach to the blind identification of multichannel communication systems by using partial knowledge of the channel. The received signal is first processed by a filter constructed by the known component of the channel and then a blind identification algorithm based on the second-order statistics is applied to the filtered signal. It is shown that, if the unknown component satisfies the identifiability condition, the channel can be identified even though the channel does not satisfy the identifiability condition. Simulation results are presented to show the performance of the proposed approach. A comparison to the existing approaches is also presented.

This paper is concerned with blind identification of a nonminimum phase transfer function model. By over-sampling the output at a higher rate than the input, it is shown that its input-output relation can be described by a single input multiple output model (SIMO) with a common denominator polynomial. Based on the model expression, we present an algorithm to estimate numerator polynomials and common denominator polynomial in a blind manner. Furthermore, identifiability of the proposed scheme is clarified, and some numerical results are given for demonstrating its effectiveness.

A new identification algorithm based on output over-sampling scheme is proposed for a IIR model whose input signal can not be available directly. By using only an output signal sampled at higher rate than unknown input, parameters of the IIR model can be identified. It is clarified that the consistency of the obtained parameter estimates is assured under some specified conditions. Further an efficient recursive algorithm for blind parameter estimation is also given for practical applications. Simulation results demonstrate its effectiveness in both system and channel identification.

This paper presents a variant of the "Third-Order Recursion (TOR)" method for bispectral estimation of transfer-function parameters of a non-minimum-phase all-poles system. The modification is based on the segmentation of system-output data into coupled records, instead of independent records. It consists of considering the available data at the left and the right of each record as not null and taking them as the data corresponding to the preceding and succeeding record respectively. The proposed variant can also be interpreted as a "Constrained Third-Order Mean (CTOM)" method with a new segmentation in overlap records. Simulation results show that this new segmentation procedure gives more precise system parameters than the TOR and CTOM methods, to be obtained. Finally, in order to justify the use of bispectral techniques, the influence of added white and colored Gaussian noise on the parameter estimation is also considered.

This paper presents a new blind identification method of nonminimum phase FIR systems and an adaptive blind equalization for PAM/QAM inputs without employing higher-order statistics. They are based on the observation that the absolute mean of a second-order white sequence can measure whether the sequence is higher-order white or not. The proposed methods are new alternatives to many higher-order statistics approaches. Some computer simulations show that the absolute mean is exactly estimated and the proposed methods can overcome the disadvantages of the higher-order statistics approaches.