Robust Range Ambiguous Deceptive Target Suppression Based on Covariance Matrix Reconstruction

When the deceptive targets are in the ambiguious range bin but are received at the same range gate with the desired target by the array, the traditional multiple-input multiple-output (MIMO) radar is not able to discriminate between them. Based on the unique range-dependent beampattern of the frequency diverse array (FDA)-MIMO radar, we propose a novel robust mainlobe deceptive target suppression method based on covariance matrix reconstruction to form nulls at the frequency points of the transmit–receive domain where deceptive targets are located. First, the proposed method collects the deceptive targets and noise information in the transmit–receive frequency domain to reconstruct the jammer-noise covariance matrix (JNCM). Then, the covariance matrix of the desired target is constructed in the desired target region, which is assumed to already be known. The transmit–receive steering vector (SV) of the desired target is estimated to be the dominant eigenvector of the desired target covariance matrix. Finally, the weighting vector of the receive beamformer is calculated by combining the reconstructed JNCM and the estimated desired target SV. By implementing the weighting vector at the receiving end, the deceptive targets can be effectively suppressed. The simulation results demonstrate that the proposed method is robust to SV mismatches and provides a signal-to-jamming-plus-noise ratio (SJNR) output that is close to the optimal.

Misalignment of selection, plasticity, and among-individual variation: A test of theoretical predictions with Peromyscus maniculatus.

Genetic variation and phenotypic plasticity are predicted to align with selection surfaces, a prediction that has rarely been empirically tested. Understanding the relationship between sources of phenotypic variation, i.e. genetic variation and plasticity, with selection surfaces improves our ability to predict a population's ability to adapt to a changing environment and our understanding of how selection has shaped phenotypes. Here, we estimated the (co)variances among three different behaviors (activity, aggression, and anti-predator response) in a natural population of deer mice (Peromyscus maniculatus). Using multi-response generalized mixed effects models, we divided the phenotypic covariance matrix into among- and within-individual matrices. The among-individual covariances includes genetic and permanent environmental covariances (e.g. developmental plasticity) and is predicted to align with selection. Simultaneously, we estimated the within-individual (co)variances, which include reversible phenotypic plasticity. To determine whether genetic variation, plasticity and selection align in multivariate space we calculated the dimensions containing the greatest among-individual variation and the dimension in which most plasticity was expressed (i.e. the dominant eigenvector for the among- and within-individual covariance matrices respectively). We estimated selection coefficients based on survival estimates from a mark-recapture model. Alignment between the dominant eigenvectors of behavioural variation and the selection gradient was estimated by calculating the angle between them, with an angle of 0 indicating perfect alignment. The angle between vectors ranged from 68 to 89, indicating that genetic variation, phenotypic plasticity, and selection are misaligned in this population. This misalignment could be due to the behaviors being close to their fitness optima, which is supported by low evolvabilities, or because of low selection pressure on these behaviors.

Development of a Method for Data Dimensionality Reduction in Loop Closure Detection: An Incremental Approach

SUMMARY This article proposes a method for incremental data dimensionality reduction in loop closure detection for robotic autonomous navigation. The approach uses dominant eigenvector concept for: (a) spectral description of visual datasets and (b) representation in low dimension. Unlike most other papers on data dimensionality reduction (which is done in batch mode), our method combines a sliding window technique and coordinate transformation to achieve dimensionality reduction in incremental data. Experiments in both simulated and real scenarios were performed and the results are suitable.

Determining Equivalent Resistance and Nodal Potentials of a Resistive Circuit Using a Reduced Transition Matrix

Several methods have been developed in order to solve electrical circuits consisting of resistors and an ideal voltage source. A correspondence with random walks avoids difficulties caused by choosing directions of currents and signs in potential differences. Starting from the random-walk method, we introduce a reduced transition matrix of the associated Markov chain whose dominant eigenvector alone determines the electric potentials at all nodes of the circuit and the equivalent resistance between the nodes connected to the terminals of the voltage source. Various means to find the eigenvector are developed from its definition. A few example circuits are solved in order to show the usefulness of the present approach.

DOMINANT EIGENVECTOR AND EIGENVALUE ALGORITHM IN SPARSE NETWORK SPECTRAL CLUSTERING

The sparse network spectrum clustering problem is studied in this paper. It tries to analyze and improve the sparse network spectrum clustering algorithm from the main feature pair algorithm. The main feature pair algorithm in the matrix calculation is combined with the spectral clustering algorithm to explore the application of the main feature pair algorithm on the network adjacency matrix. The defects of traditional main features are analyzed when the algorithm Power is used on the network of special structural features, and the advantages of the new algorithm SII algorithm is proved. The sparse network spectral clustering algorithm in this paper is based on the Score algorithm, and the main features of the algorithm are refined, analyzed and improved.

Correlation Matrices with the Perron Frobenius Property

This paper investigates conditions under which correlation matrices have a strictly positive dominant eigenvector. The sufficient conditions, from the Perron-Frobenius theorem, are that all the matrix entries are positive. The conditions for a correlation matrix with some negative entries to have a strictly positive dominant eigenvector are examined. The special structure of correlation matrices permits obtaining of detailed analytical results for low dimensional matrices. Some specific results for the $n$-by-$n$ case are also derived. This problem was motivated by an application in portfolio theory.

Rank monotonicity in centrality measures

A measure of centrality isrank monotoneif after adding an arcx→y, all nodes with a score smaller than (or equal to)yhave still a score smaller than (or equal to)y. If, in particular, all nodes with a score smaller than or equal toyget a score smaller thany(i.e., all ties withyare broken in favor ofy), the measure is calledstrictly rank monotone. We prove that harmonic centrality is strictly rank monotone, whereas closeness is just rank monotone on strongly connected graphs, and that some other measures, including betweenness, are not rank monotone at all (sometimes not even on strongly connected graphs). Among spectral measures,dampedscores such as Katz's index and PageRank are strictly rank monotone on all graphs, whereas the dominant eigenvector is strictly monotone on strongly connected graphs only.