- DOA Estimation Using Deep Neural Network with Angular Sliding ...🔍
- Direction|of|Arrival Estimation Using A Machine Learning Framework🔍
- A Deep Learning Architecture for Broadband DOA Estimation🔍
- A low computational complexity DOA estimation using sum ...🔍
- Deep adaptive temporal network 🔍
- Deep learning|based DOA estimation using CRNN for underwater ...🔍
- Data|Driven DOA Estimation Methods Based on Deep Learning for ...🔍
- Random forest|based multipath parameter estimation🔍
Deep Learning|Based Multipath DoAs Estimation Method for ...
DOA Estimation Using Deep Neural Network with Angular Sliding ...
A detector network and an estimator network are followed for each sub-region, enabling high estimation accuracy and the number of sources. Simulation and real ...
Direction-of-Arrival Estimation Using A Machine Learning Framework
... DoA estimation method based on deep neural networks that could achieve better estimation results than the methods based on support vector machine. (SVM).
A Deep Learning Architecture for Broadband DOA Estimation
An efficient neural network-based approach for broadband direction of arrival (DOA) estimation is presented, and the proposed CNN estimator further reduces ...
A low computational complexity DOA estimation using sum ...
In this paper, an efficient DOA estimation approach based on Sum/Difference pattern is merged with deep neural network. Fully learn the potential features of ...
Deep adaptive temporal network (DAT-Net): an effective deep ...
... learning model for parameter estimation of radar multipath ... based importance evaluation algorithm for dynamic learning of importance weights.
DeepAoANet: Learning Angle of Arrival from Software Defined ...
Deep Learning method is able to estimate accurately both the number ... Liu, A Novel Phase. Enhancement Method for Low-Angle Estimation Based on ...
Deep learning-based DOA estimation using CRNN for underwater ...
In the marine environment, estimating the direction of arrival (DOA) is challenging because of the multipath signals and low signal-to-noise ratio (SNR).
Data-Driven DOA Estimation Methods Based on Deep Learning for ...
Because of the advantages of deep learning technology, this paper proposes two categories of data-driven DOA estimation methods for underwater acoustic vector ...
Random forest-based multipath parameter estimation | GPS Solutions
Multipath is recognized as one of the major error sources for GNSS urban navigation. This study proposes a random forest (RF)-based ...
LFM based Wideband DOA Estimation using Deep Neural Network ...
This work focuses on deep learning-based wideband direction-of-arrival (DoA) estimation for a wideband in particular LFM in case of extreme ...
How useful is differential geometry and topology to deep learning?
A "roadmap type" introduction is given by Roger Grosse in Differential geometry for machine learning. Differential geometry is all about ...
DOA Estimation Method in Multipath Environment - ProQuest
Simulation results validate its effectiveness; meanwhile, the good performances of the proposed method in terms of resolution probability, detection probability ...
Multi-DOA Estimation Based on the KR Image Tensor and Improved ...
These machine learning based methods are also used for dealing with special signal forms, such as the sparse Bayesian learning (SBL) method for ...
Machine Learning-Based DOA Estimation for Correlated Signals
To try to avoid detection, potential targets may fly close to the reflective sea surface giving rise to multipath propagation and difficulties estimating.
Optimized Position Estimation in Mobile Multipath Environments ...
The ISE algorithm employs a supervised self-organizing map (SOM) neural network machine-learning algorithm (Kohonen, 1990) to compute the probability of a ...
A Lightweight Deep Learning-Based Algorithm for Array ...
Array imperfections will lead to serious performance degradation of the deep neural network(DNN) based direction of arrival (DOA) estimation in the low ...
Convolutional Neural Network-based DOA estimation Using Non ...
Recently, the machine learning technique is introduced as an effective approach to solving the DOA estimation problem, for example, support vector machine ...
10. LOS DOA RMSE comparison of standard ESPRIT versus DNN- based DOA estimator for all experiments and each LTE eNodeB: (i) experi-.
Deep Learning for DOA Estimation: Novel Neural Network ...
The crux of this research lies in leveraging the inherent strength of neural networks to estimate. DOA in the presence of multipath communications and low SNR ...
3 Deep learning‑based DOA estimation method for HAD‑OSA ... Wang, Deep learning-based multipath DOAs estimation method for MM wave massive MIMO systems in low.