AIMC Topic: Radar

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CNN-LRP: Understanding Convolutional Neural Networks Performance for Target Recognition in SAR Images.

Sensors (Basel, Switzerland)
Target recognition is one of the most challenging tasks in synthetic aperture radar (SAR) image processing since it is highly affected by a series of pre-processing techniques which usually require sophisticated manipulation for different data and co...

SAR ATR for Limited Training Data Using DS-AE Network.

Sensors (Basel, Switzerland)
Although automatic target recognition (ATR) with synthetic aperture radar (SAR) images has been one of the most important research topics, there is an inherent problem of performance degradation when the number of labeled SAR target images for traini...

Classification of Space Objects by Using Deep Learning with Micro-Doppler Signature Images.

Sensors (Basel, Switzerland)
Radar target classification is an important task in the missile defense system. State-of-the-art studies using micro-doppler frequency have been conducted to classify the space object targets. However, existing studies rely highly on feature extracti...

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning.

Sensors (Basel, Switzerland)
Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of var...

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

Computational intelligence and neuroscience
In the field of electronic countermeasure, the recognition of radar signals is extremely important. This paper uses GNU Radio and Universal Software Radio Peripherals to generate 10 classes of close-to-real multipulse radar signals, namely, Barker, C...

The accuracy and predictability of micro Doppler radar signature projection algorithm measuring functional movement in NCAA athletes.

Gait & posture
BACKGROUND: Development of accessible cost-effective technology to objectively, reliably, and accurately predict musculoskeletal injury risk could aid the effort to prevent chronic pain and disability. Recent work on micro-Doppler radar suggests it m...

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...