The purpose of this letter is to solve the space-time off-grid problem caused by the extremely inhomogeneous clutter spectrum under the space/airborne bistatic system. For the estimation of inhomogeneous clutter spectrum, the method of sparse recovery (SR) is usually adopted. However, performance of sparse recovery may degrade since the real clutter point often does not fall in the center of the space-time grid. To deal with this issue, a local mesh splitting algorithm is proposed in this letter. First, select the space-time vector atoms which are most relevant to the residuals by subspace projection. Then, iterative local mesh splitting is performed around the selected atoms to match the real clutter points. Finally, the clutter subspace is reconstructed from all matched atoms selected by global and local iterations. The simulation results show that the method can obtain better accuracy with lower computational effort.

MOTIVATION: With the development of high-throughput sequencing techniques for 16S-rRNA gene profiling, the analysis of microbial communities is becoming more and more attractive and reliable. Inferring the direct interaction network among microbial communities helps in the identification of mechanisms underlying community structure. However, the analysis of compositional data remains challenging by the relative information conveyed by such data, as well as its high dimensionality. RESULTS: In this article, we first propose a novel loss function for compositional data called CD-trace based on D-trace loss. A sparse matrix estimator for the direct interaction network is defined as the minimizer of lasso penalized CD-trace loss under positive-definite constraint. An efficient alternating direction algorithm is developed for numerical computation. Simulation results show that CD-trace compares favorably to gCoda and that it is better than sparse inverse covariance estimation for ecological association inference (SPIEC-EASI) (hereinafter S-E) in network recovery with compositional data. Finally, we test CD-trace and compare it to the other methods noted above using mouse skin microbiome data. AVAILABILITY AND IMPLEMENTATION: The CD-trace is open source and freely available from https://github.com/coamo2/CD-trace under GNU LGPL v3. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this letter, a reduced-rank beamforming method is presented based on knowledge-aided joint iterative optimization. The proposed adaptive reduced-rank processing is realized by joint optimization for the reduced-dimension matrix and beamforming weight vector. Meanwhile, recursively updating the prior covariance matrix using the spatial spectrum reconstruction technology and the weighted processing improve the estimation precision of the array covariance matrix. The simulation results show that the proposed method is robust to the dimension of the reduced-dimension matrix and significantly improves the output signal-to-interference-plus-noise ratio (SINR) of the adaptive beamformer under the condition of few samples.