Results 71 to 80 of about 7,085,552 (171)
Sparse Bayesian Learning for DOA Estimation with Mutual Coupling
Sensors, 2015 Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known.
Jisheng Dai +3 more
doaj +1 more source
Direction of Arrival Estimation With Low Resolution Quantised Data: A Taxonomy and Survey
IET Radar, Sonar &Navigation, Volume 19, Issue 1, January/December 2025.This paper presents a comprehensive survey of Direction of Arrival (DoA) estimation techniques that utilise one‐bit and low‐resolution data. ABSTRACT This paper presents a comprehensive survey of Direction of Arrival (DoA) estimation techniques that utilise one‐bit and low‐resolution data.
Yasin Azhdari, Mahmoud Farhang
wiley +1 more source
IEEE Access, 2019 Micro Doppler analysis of spin stabilized objects is of a great significance for attitude estimation and recognition of space targets. In practice, the radar cannot dwell on one target in a long interval continuously.
Ling Hong, Fengzhou Dai, Xili Wang
doaj +1 more source
Robust User Identification in Massive MIMO Systems Using Compressed Sensing Techniques
Journal of Electrical and Computer Engineering, Volume 2025, Issue 1, 2025.In 5G and beyond 5G wireless communication networks, massive multiple‐input multiple‐output (MIMO) technologies are essential for improved energy and spectrum efficiency. These systems are very effective at channel estimation and active user detection in situations with many users, and only a small percentage of them send data during a given time frame.
Ubaid Umar +3 more
wiley +1 more source
Enhanced Sparse Bayesian Learning Methods with Application to Massive MIMO Channel Estimation [PDF]
We consider the problem of sparse channel estimation in massive multiple-input multiple-output systems. In this context, we propose an enhanced version of the sparse Bayesian learning (SBL) framework, referred to as enhanced SBL (E-SBL), which is based ...
Arjas, Arttu, Atzeni, Italo
core +4 more sources
Structural Control and Health Monitoring, Volume 2025, Issue 1, 2025.This paper presents a novel unsupervised method for structural damage diagnosis, which transforms the problem of structural damage diagnosis into the problem of identifying anomalous data in monitoring data. The method establishes the sensor interrelationships based on the graph structure, optimizes the hyperparameters of the graph neural network (GNN)
Wen-Sheng Zhang +4 more
wiley +1 more source
Structural Control and Health Monitoring, Volume 2025, Issue 1, 2025.Bridge structural health monitoring (BSHM) has consistently been a research hotspot in civil engineering. The field of BSHM has experienced a significant transition from traditional manual inspections to an advanced integration of artificial intelligence (AI), culminating in the current peak with data‐driven AI methodologies.
Yunchao Tang +5 more
wiley +1 more source
Bayesian Orthogonal Component Analysis for Sparse Representation [PDF]
, 2010 This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This undercomplete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with ...
Nicolas Dobigeon +3 more
core +1 more source
Bayesian Learning for Joint Sparse OFDM Channel Estimation and Data Detection
, 2010 The impulse response of a typical wireless multipath channel can be modeled as a tapped delay line filter whose non-zero components are sparse relative to the channel delay spread.
Prasad, Ranjitha, Murthy, Chandra R
core