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Found 26 papers, 0 papers with code
Study of Enhanced MISC-Based Sparse Arrays with High uDOFs and Low Mutual Coupling
In this letter, inspired by the maximum inter-element spacing (IES) constraint (MISC) criterion, an enhanced MISC-based (EMISC) sparse array (SA) with high uniform degrees-of-freedom (uDOFs) and low mutual-coupling (MC) is proposed, analyzed and discussed in detail.
Efficient Covariance Matrix Reconstruction with Iterative Spatial Spectrum Sampling
This work presents a cost-effective technique for designing robust adaptive beamforming algorithms based on efficient covariance matrix reconstruction with iterative spatial power spectrum (CMR-ISPS).
Study of Robust Adaptive Beamforming with Covariance Matrix Reconstruction Based on Power Spectral Estimation and Uncertainty Region
In this work, a simple and effective robust adaptive beamforming technique is proposed for uniform linear arrays, which is based on the power spectral estimation and uncertainty region (PSEUR) of the interference plus noise (IPN) components.
Study of Novel Sparse Array Design Based on the Maximum Inter-Element Spacing Criterion
A novel sparse array (SA) structure is proposed based on the maximum inter-element spacing (IES) constraint (MISC) criterion.
Study of Robust Sparsity-Aware RLS algorithms with Jointly-Optimized Parameters for Impulsive Noise Environments
This paper proposes a unified sparsity-aware robust recursive least-squares RLS (S-RRLS) algorithm for the identification of sparse systems under impulsive noise.
Study of filtered-x logarithmic recursive least $p$-power algorithm
For active impulsive noise control, a filtered-x recursive least $p$-power (FxRLP) algorithm is proposed by minimizing the weighted summation of the $p$-power of the \emph{a posteriori} errors.
Study of List-Based OMP and an Enhanced Model for Direction Finding with Non-Uniform Arrays
This paper proposes an enhanced coarray transformation model (EDCTM) and a mixed greedy maximum likelihood algorithm called List-Based Maximum Likelihood Orthogonal Matching Pursuit (LBML-OMP) for direction-of-arrival estimation with non-uniform linear arrays (NLAs).
Robust Adaptive Filtering Based on Exponential Functional Link Network
The exponential functional link network (EFLN) has been recently investigated and applied to nonlinear filtering.
Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals
In this work, we present an energy-efficient distributed learning framework using low-resolution ADCs and coarsely quantized signals for Internet of Things (IoT) networks.
Low-Cost Maximum Entropy Covariance Matrix Reconstruction Algorithm for Robust Adaptive Beamforming
In this letter, we present a novel low-complexity adaptive beamforming technique using a stochastic gradient algorithm to avoid matrix inversions.
Study of Energy-Efficient Distributed RLS-based Learning with Coarsely Quantized Signals
In this work, we present an energy-efficient distributed learning framework using coarsely quantized signals for Internet of Things (IoT) networks.
Study of Diffusion Normalized Least Mean M-estimate Algorithms
This work proposes diffusion normalized least mean M-estimate algorithm based on the modified Huber function, which can equip distributed networks with robust learning capability in the presence of impulsive interference.
Study of Robust Two-Stage Reduced-Dimension Sparsity-Aware STAP with Coprime Arrays
Simulation results show that the proposed algorithm is robust to prior knowledge errors and can provide good clutter suppression performance in low sample support.
Study of Distributed Robust Beamforming with Low-Rank and Cross-Correlation Techniques
In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and cross-correlation techniques.
Compressed Sensing with Probability-based Prior Information
This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing system.
Robust DCD-Based Recursive Adaptive Algorithms
The dichotomous coordinate descent (DCD) algorithm has been successfully used for significant reduction in the complexity of recursive least squares (RLS) algorithms.
Study of Robust Distributed Diffusion RLS Algorithms with Side Information for Adaptive Networks
This work develops robust diffusion recursive least squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise.
Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agents
This work develops a robust diffusion recursive least squares algorithm to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise.
Direction Finding Based on Multi-Step Knowledge-Aided Iterative Conjugate Gradient Algorithms
In this work, we present direction-of-arrival (DoA) estimation algorithms based on the Krylov subspace that effectively exploit prior knowledge of the signals that impinge on a sensor array.
Study of Sparsity-Aware Subband Adaptive Filtering Algorithms with Adjustable Penalties
We propose two sparsity-aware normalized subband adaptive filter (NSAF) algorithms by using the gradient descent method to minimize a combination of the original NSAF cost function and the l1-norm penalty function on the filter coefficients.
Study of Set-Membership Adaptive Kernel Algorithms
In the last decade, a considerable research effort has been devoted to developing adaptive algorithms based on kernel functions.
Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
An analysis of the MSE of the reshaped data covariance matrix is carried out along with comparisons between computational complexities of the proposed and existing algorithms.
Study of Robust Distributed Beamforming Based on Cross-Correlation and Subspace Projection Techniques
In this work, we present a novel robust distributed beamforming (RDB) approach to mitigate the effects of channel errors on wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output.
Signal Processing Information Theory Information Theory
Study of Set-Membership Kernel Adaptive Algorithms and Applications
Adaptive algorithms based on kernel structures have been a topic of significant research over the past few years.
Distributed Low-Rank Estimation Based on Joint Iterative Optimization in Wireless Sensor Networks
This paper proposes a novel distributed reduced--rank scheme and an adaptive algorithm for distributed estimation in wireless sensor networks.
Dynamic Topology Adaptation and Distributed Estimation for Smart Grids
This paper presents new dynamic topology adaptation strategies for distributed estimation in smart grids systems.
