different methods exists for jamming mitigation and we should choose a method based an the jammer type and other parameters. one of the jammers is narrow band fm jammer and we use subspace projection techniques for the suppression of this type of jammer. in subspace projection technique, we estimate the if of signals and construct the subspace vector that is orthogonal to jammer vector by incre...

Let $mathcal{A}$ be a Banach algebra with BAI and $E$ be an introverted subspace of $mathcal{A}^prime$. In this paper we study the quotient Arens regularity of $mathcal{A}$ with respect to $E$ and prove that the group algebra $L^1(G)$ for a locally compact group $G$, is quotient Arens regular with respect to certain introverted subspace $E$ of $L^infty(G)$. Some related result are given as well.

The Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is a subspace-based analysis method used in source localization or frequency estimation, originally designed in a block signal processing context. In other respects, the Projection Approximation Subspace Tracker (PAST) is a fast and robust subspace tracking method. This paper introduces a new frequency e...

The last few decades have witnessed a great success of subspace learning for face recognition. From principal component analysis (PCA) [43] and Fisher’s linear discriminant analysis [1], a dozen of dimension reduction algorithms have been developed to select effective subspaces for the representation and discrimination of face images [17, 21, 45, 46, 51]. It has demonstrated that human faces, a...

Subspace analysis methods have gained interest for identifying patterns in subspaces of high-dimensional data. Existing techniques allow to visualize and compare patterns in subspaces. However, many subspace analysis methods produce an abundant amount of patterns, which often remain redundant and are difficult to relate. Creating effective layouts for comparison of subspace patterns remains cha...

This discussion presents a new perspective of subspace independent component analysis (ICA). The notion of a function of cumulants (kurtosis) is generalized to vector kurtosis. This vector kurtosis is utilized in the subspace ICA algorithm to estimate subspace independent components. One of the main advantages of the presented approach is its computational simplicity. The experiments have shown...

This paper presents a new subspace modeling and selection approach for noisy speech recognition. In subspace modeling, we develop factor analysis (FA) for representing noisy speech. FA is a data generation model where the common factors are extracted with factor loading matrix and specific factors. We bridge the connection of FA to signal subspace (SS) approach. Interestingly, FA partitions noi...

Subspace-based methods rely on singular value decomposition (SVD) of the sample covariance matrix (SCM) to compute the array signal or noise subspace. For large array, triditional subspace-based algorithms inevitably lead to intensive computational complexity due to both calculating SCM and performing SVD of SCM. To circumvent this problem, a NyströmBased algorithm for array subspace estimation...

This article presents a new subspace-based technique for reducing the noise of signals in time-series. In the proposed approach, the signal is initially represented as a data matrix. Then using Singular Value Decomposition (SVD), noisy data matrix is divided into signal subspace and noise subspace. In this subspace division, each derivative of the singular values with respect to rank order is u...