Neural Network-Based Control of an Adaptive Radar

John-Baptiste, Peter; Johnson, Joel T.; Smith, Gerald Birney

doi:10.1109/taes.2021.3103577

Cited by 6 publications

(2 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where η represents the learning rate. Furthermore, more complex and better performance methods also exist to optimise the weights, such as the Levenberg-Marquardt algorithm [32]. At present, deep networks and their ensembles are the most advanced solutions for the majority of typical applications, including computer vision, speech processing, and image processing [14][15][16][17][18][19].…”

Section: Neural Networkmentioning

confidence: 99%

“…With backpropagation, the simple gradient descent can be used to identify the optimal weights as follows:

{\mathbf{w}}^{\prime }=\mathbf{w}-\eta \nabla L(\mathbf{w})

where η represents the learning rate. Furthermore, more complex and better performance methods also exist to optimise the weights, such as the Levenberg–Marquardt algorithm [32].…”

Section: Alternative Machine Learning Techniques For Time Delay Estim...mentioning

confidence: 99%

See 1 more Smart Citation

Deep learning‐based time delay estimation for motion compensation in synthetic aperture sonars

Chen,

Chi,

Zhang

et al. 2023

IET Radar Sonar & Navi

View full text Add to dashboard Cite

Accurate and robust time delay estimation is crucial for synthetic aperture sonar (SAS) imaging. A two‐step time delay estimation method based on displaced phase centre antenna (DPCA) micronavigation has been widely applied in motion estimation and compensation of SASs. However, the existing methods for time delay estimation are not sufficiently robust, which reduces the performance of SAS motion estimation. Deep learning is currently one of the cutting‐edge techniques and has brought about a remarkable progress in the field of underwater acoustic signal processing. In this study, a deep learning‐based time delay estimation method is introduced to SAS motion estimation and compensation. The subband processing is first applied to obtain ambiguous time delays between adjacent pings from phases of SAS echoes. Then, a lightweight neural network is utilised to construct phase unwrapping. The model of the employed neural network is trained with simulation data and applied to real SAS data. The results of time delay estimation and motion compensation demonstrate that the proposed neural network‐based method has much better performance than the two‐step and joint‐subband methods.

show abstract

Section: Neural Networkmentioning

confidence: 99%

“…With backpropagation, the simple gradient descent can be used to identify the optimal weights as follows:

{\mathbf{w}}^{\prime }=\mathbf{w}-\eta \nabla L(\mathbf{w})

where η represents the learning rate. Furthermore, more complex and better performance methods also exist to optimise the weights, such as the Levenberg–Marquardt algorithm [32].…”

Section: Alternative Machine Learning Techniques For Time Delay Estim...mentioning

confidence: 99%