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Radar

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Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition.

Sensors (Basel, Switzerland)
The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion a...

Sea Clutter Suppression Method of HFSWR Based on RBF Neural Network Model Optimized by Improved GWO Algorithm.

Computational intelligence and neuroscience
The detection performance of high-frequency surface-wave radar (HFSWR) is closely related to the suppression effect of sea clutter. To effectively suppress sea clutter, a sea clutter suppression method based on radial basis function neural network (R...

Machine Learning-Based Human Recognition Scheme Using a Doppler Radar Sensor for In-Vehicle Applications.

Sensors (Basel, Switzerland)
In this paper, we propose a Doppler spectrum-based passenger detection scheme for a CW (Continuous Wave) radar sensor in vehicle applications. First, we design two new features, referred to as an 'extended degree of scattering points' and a 'differen...

Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network.

Sensors (Basel, Switzerland)
At present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resol...

A Radar Signal Recognition Approach via IIF-Net Deep Learning Models.

Computational intelligence and neuroscience
In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general so...

High-Resolution Radar Target Recognition via Inception-Based VGG (IVGG) Networks.

Computational intelligence and neuroscience
Aiming at high-resolution radar target recognition, new convolutional neural networks, namely, Inception-based VGG (IVGG) networks, are proposed to classify and recognize different targets in high range resolution profile (HRRP) and synthetic apertur...

Hybrid SVM-CNN Classification Technique for Human-Vehicle Targets in an Automotive LFMCW Radar.

Sensors (Basel, Switzerland)
Human-vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-...

Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review.

Sensors (Basel, Switzerland)
Attention to the natural environment is equivalent to observing the space in which we live. Plant roots, which are important organs of plants, require our close attention. The method of detecting root system without damaging plants has gradually beco...

Doppler-Spectrum Feature-Based Human-Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor.

Sensors (Basel, Switzerland)
In this paper, we propose a Doppler-spectrum feature-based human-vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering poi...

Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L-Regularization.

Computational intelligence and neuroscience
Though Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) via Convolutional Neural Networks (CNNs) has made huge progress toward deep learning, some key issues still remain unsolved due to the lack of sufficient samples and robust mode...