This paper deals with the decoupling problems of unknown, measurable, and previewed signals. First, well-known solutions of unknown and measurable disturbance decoupling problems are recalled. Then, new necessary and sufficient constructive conditions for the previewed signal decoupling problem are proposed. The discrete-time case is considered. In this domain, previewing a signal by p steps me...

This paper deals with the performance of blind multiuser detectors for CDMA is analyzed. The blind multiuser detectors are Direct Matrix Inversion (DMI) blind detector and subspace blind detector. The performance analysis is performed by means of the Signal to Interference Noise Ration (SINR) and Bit Error Rate (BER). The numerical results are plotted as variation of SINR Vs SNR and ρ , SINR wi...

A novel approach is proposed in order to reduce the number of sweeps (trials) required for the efficient extraction of the brain evoked potentials (EPs). This approach is developed by combining both the Wiener filtering and the subspace methods. First, the signal subspace is estimated by applying the singularvalue decomposition (SVD) to an enhanced version of the raw data obtained by Wiener fil...

In this study, the Direction Of Departure (DOD) and Arrival (DOA) of signals detection for Multi-Input Multi-Output (MIMO) radar is discussed. A novel signal subspace reconstruction model to match obtained based on covariance matrix array output developed enhance performance DOD DOA detection. scheme, technology beamforming first introduced define an objective space in mathematics targets be de...

In this paper we utilize the gamma neural model to improve the signal to noise ratio (SNR) of broadband signals corrupted by white noise. The projection of a noisy signal onto the signal subspace can not remove the noise in the subspace. A focus gamma network, when trained as a nonlinear predictor of the projected trajectory, reduces this noise further. The property of adaptive memory depth of ...

In this paper, several new applications for subspace methods are introduced, including: (1) a subspace method used for overcoming generalization/overrtting problem for applications such as face recognition, (2) multiple subspaces constructed from the original sub-space to accommodate the inputs distorted by scaling, rotating, translating etc, and (3) a subspace framework based on which the orig...

Signal parameter estimation from sensor array data is a problem that is encountered in many engineering applications. Under the assumption of Gaussian distributed emitter signals, the so-called stochastic maximum likelihood (ML) technique is known to be statistically efficient, i.e., the estimation error covariance attains the Cramer-Rao bound (CRB) asymptotically. Herein, it is shown that also...