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A Neural Network Target Detector with Partial CA-CFAR Supervised Training
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.eswa.2014.12.055openalex-snapshot
MLP-CFAR for improving coherent radar detectors robustness in variable scenarios
inferredcites →https://doi.org/10.1049/ip-rsn:20050103openalex-snapshot
Application of neural networks to radar signal detection in K -distributed clutter
inferredcites →https://doi.org/10.1109/18.335950openalex-snapshot
Optimality of the cell averaging CFAR detector
inferredcites →https://doi.org/10.1109/7.7185openalex-snapshot
Analysis of CFAR processors in nonhomogeneous background
inferredcites →https://doi.org/10.1109/78.650111openalex-snapshot
Neural networks for signal detection in non-Gaussian noise
inferredcites →https://doi.org/10.1109/acc.1994.752367openalex-snapshot
A robust neural network scheme for constant false alarm rate processing for target detection in clutter environment
inferredcites →https://doi.org/10.1109/elecsym.2015.7380855openalex-snapshot
Switching CA/OS CFAR using neural network for radar target detection in non-homogeneous environment
inferredcites →https://doi.org/10.1109/vtcfall.2015.7390978openalex-snapshot
Improved Detection by Peak Shape Recognition Using Artificial Neural Networks
inferredcites →https://doi.org/10.1117/1.2819119openalex-snapshot
Book Review: Pattern Recognition and Machine Learning
inferredcites →https://doi.org/10.5860/choice.44-5091openalex-snapshot
Pattern recognition and machine learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4590633openalex-extracted
Cfarnet: Deep Learning for Target Detection with Constant False Alarm Rate
inferred← citeshttps://doi.org/10.1007/s00521-021-05753-wopenalex-snapshot
A method of radar target detection based on convolutional neural network
inferred← citeshttps://doi.org/10.1016/j.phycom.2023.102014openalex-snapshot
Deep learning-based circular disk type radar target detection in complex environment
inferred← citeshttps://doi.org/10.1049/rsn2.70011openalex-snapshot
Machine Learning Doppler‐Tolerant One‐Bit Radar Detectors
inferred← citeshttps://doi.org/10.1088/1361-6501/acb608openalex-snapshot
Adaptive filter entropy monitoring method for scalar magnetic detection using optically pumped magnetometers
inferred← citeshttps://doi.org/10.1109/access.2020.3044453openalex-snapshot
Artificial Intelligence Aided Electronic Warfare Systems- Recent Trends and Evolving Applications
inferred← citeshttps://doi.org/10.1109/ciss56502.2023.10089751openalex-snapshot
Learning of Doppler Tolerant Radar Detectors for Noise Waveforms
inferred← citeshttps://doi.org/10.1109/ijcnn48605.2020.9207080openalex-snapshot
Range-Doppler Detection in Automotive Radar with Deep Learning
inferred← citeshttps://doi.org/10.1109/jsen.2025.3594801openalex-snapshot
Efficient Recursive Convolutional Target Detector for FMCW Radar With Implementation on a Programmable Deep Learning Processor Unit
inferred← citeshttps://doi.org/10.1109/maes.2021.3140064openalex-snapshot
Deep Learning Approach for Fixed and Rotary-Wing Target Detection and Classification in Radars
inferred← citeshttps://doi.org/10.1109/meco62516.2024.10577789openalex-snapshot
CA-CFAR is Convolution: Fast Target Detection with Machine Learning Accelerator
inferred← citeshttps://doi.org/10.1109/radar.2019.8835765openalex-snapshot
GO-CFAR Trained Neural Network Target Detectors
inferred← citeshttps://doi.org/10.1109/radar52380.2025.11031690openalex-snapshot
Neural Network Aided False and Ghost Detection Suppression
inferred← citeshttps://doi.org/10.1109/spawc51304.2022.9834032openalex-snapshot
Learning to Detect with Constant False Alarm Rate
inferred← citeshttps://doi.org/10.1109/taes.2023.3274101openalex-snapshot
A Metacognitive Approach to Adaptive Radar Detection
inferred← citeshttps://doi.org/10.1109/tccn.2024.3391327openalex-snapshot
Learning and Model-Based Approaches for Radar Target Detection
inferred← citeshttps://doi.org/10.1109/vtcfall.2019.8891420openalex-snapshot
DL-CFAR: A Novel CFAR Target Detection Method Based on Deep Learning
inferred← citeshttps://doi.org/10.1186/s13634-021-00801-yopenalex-snapshot
A neural network framework for binary classification of radar detections
inferred← citeshttps://doi.org/10.23919/eusipco47968.2020.9287495openalex-snapshot
Training of Neural Network Target Detectors Mentored by SO-CFAR
inferred← citeshttps://doi.org/10.26866/jees.2022.1.r.56openalex-snapshot
Convolutional Neural Network-Based Target Detector Using Maxpooling and Hadamard Division Layers in FM-Band Passive Coherent Location
inferred← citeshttps://doi.org/10.3390/electronics11010156openalex-snapshot
Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review
inferred← citeshttps://doi.org/10.3390/electronics11152319openalex-snapshot
Distributed Radar Target Detection Based on RF-SSA in Non-Gaussian Noise
inferred← citeshttps://doi.org/10.3390/jmse13081475openalex-snapshot
A Novel iTransformer-Based Approach for AIS Data-Assisted CFAR Detection
inferred← citeshttps://doi.org/10.3390/rs16020332openalex-snapshot
Target Detection Method for High-Frequency Surface Wave Radar RD Spectrum Based on (VI)CFAR-CNN and Dual-Detection Maps Fusion Compensation
inferred← citeshttps://doi.org/10.3390/sym11121482openalex-snapshot
Robust CFAR Detection for Multiple Targets in K-Distributed Sea Clutter Based on Machine Learning
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