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Found 23 papers, 0 papers with code
One-Bit Target Detection in Collocated MIMO Radar with Colored Background Noise
One-bit sampling has emerged as a promising technique in multiple-input multiple-output (MIMO) radar systems due to its ability to significantly reduce data volume and processing requirements.
A semidefinite programming approach for robust elliptic localization
This short communication addresses the problem of elliptic localization with outlier measurements, whose occurrences are prevalent in various location-enabled applications and can significantly compromise the positioning performance if not adequately handled.
Sparse array design for MIMO radar in multipath scenarios
To improve the imaging of both direct path and multipath targets, we introduce several new criteria for MIMO radar sparse linear array (SLA) designs for multipath scenarios.
Low-Rank Tensor Completion via Novel Sparsity-Inducing Regularizers
To alleviate the bias generated by the l1-norm in the low-rank tensor completion problem, nonconvex surrogates/regularizers have been suggested to replace the tensor nuclear norm, although both can achieve sparsity.
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion
Applying half-quadratic optimization to loss functions can yield the corresponding regularizers, while these regularizers are usually not sparsity-inducing regularizers (SIRs).
Robust matrix completion via Novel M-estimator Functions
M-estmators including the Welsch and Cauchy have been widely adopted for robustness against outliers, but they also down-weigh the uncontaminated data.
Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer
Moreover, the closed-form solution to its Moreau envelope, namely, the proximity operator, is derived.
Network Topology Inference with Sparsity and Laplacian Constraints
We tackle the network topology inference problem by utilizing Laplacian constrained Gaussian graphical models, which recast the task as estimating a precision matrix in the form of a graph Laplacian.
One-Bit Spectrum Sensing for Cognitive Radio
Theoretical analysis is then performed and our results show that the performance loss of the proposed detector is approximately $2$ dB ($\pi/2$) compared to detectors employing $\infty$-bit ADCs when SNR is low.
Robust time-of-arrival localization via ADMM
This article considers the problem of source localization (SL) using possibly unreliable time-of-arrival (TOA) based range measurements.
One-Bit Covariance Reconstruction with Non-zero Thresholds: Algorithm and Performance Analysis
To mitigate this issue, we present a recovery scheme that incorporates time-varying thresholds.
Globally Optimized TDOA High Frequency Source Localization Based on Quasi-Parabolic Ionosphere Modeling and Collaborative Gradient Projection
We investigate the problem of high frequency (HF) source localization using the time-difference-of-arrival (TDOA) observations of ionosphere-refracted radio rays based on quasi-parabolic (QP) modeling.
Disproving Sum-Difference Co-Array Property
The recently published paper by Gupta and Agrawal [1] exploited the sum-difference co-array (SDCA) to enhance the virtual aperture of sparse arrays.
Sparse Array Beamformer Design via ADMM
Moreover, the proposed ADMM algorithm outperforms the SDR, SDR-V, and SCA methods, in terms of computational complexity.
Convergence Analysis of Consensus-ADMM for General QCQP
We analyze the convergence properties of the consensus-alternating direction method of multipliers (ADMM) for solving general quadratically constrained quadratic programs.
Low-Rank and Row-Sparse Decomposition for Joint DOA Estimation and Distorted Sensor Detection
Distorted sensors could occur randomly and may lead to the breakdown of a sensor array system.
Off-Grid Direction-of-Arrival Estimation Using Second-Order Taylor Approximation
The problem of off-grid direction-of-arrival (DOA) estimation is investigated.
Nonlinear Tensor Ring Network
Experimental results demonstrate the effectiveness and superiority of the proposed NTRN for image classification using two basic neural networks, LeNet-5 and VGG-11 on three datasets, viz.
Neurodynamic TDOA localization with NLOS mitigation via maximum correntropy criterion
In this paper, we exploit the maximum correntropy criterion (MCC) to robustify the traditional time-difference-of-arrival (TDOA) location estimator in the presence of non-line-of-sight (NLOS) propagation conditions.
Maximum correntropy criterion for robust TOA-based localization in NLOS environments
We investigate the problem of time-of-arrival (TOA) based localization under possible non-line-of-sight (NLOS) propagation conditions.
TDOA-based localization with NLOS mitigation via robust model transformation and neurodynamic optimization
This paper revisits the problem of locating a signal-emitting source from time-difference-of-arrival (TDOA) measurements under non-line-of-sight (NLOS) propagation.
Sparse Bayesian Learning Approach for Discrete Signal Reconstruction
By integrating the discretization enforcing prior into the SBL framework and applying the variational Bayesian inference (VBI) methodology, we devise an alternating optimization algorithm to jointly characterize the finite-alphabet feature and reconstruct the unknown signal.
l0-norm Based Centers Selection for Training Fault Tolerant RBF Networks and Selecting Centers
The aim of this paper is to train an RBF neural network and select centers under concurrent faults.
