AIMC Topic: Radar

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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...

Applying machine learning to forecast daily Ambrosia pollen using environmental and NEXRAD parameters.

Environmental monitoring and assessment
Approximately 50 million Americans have allergic diseases. Airborne plant pollen is a significant trigger for several of these allergic diseases. Ambrosia (ragweed) is known for its abundant production of pollen and its potent allergic effect in Nort...

Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.

Environmental monitoring and assessment
Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the rise due to increased pollen levels caused by global warming and the spread of highly invasive weeds. The production, release, and dispersal of pollen de...

A Fuzzy Comprehensive CS-SVR Model-based health status evaluation of radar.

PloS one
The purpose of Fuzzy Comprehensive CS-SVR Model (FCCS-SVR) is to evaluate and monitor the health status of a radar equipment and then keep its safe operation. Due to reasons such as few samples, slow changes and the nonlinear structure of data of fau...

Using a stacked-autoencoder neural network model to estimate sea state bias for a radar altimeter.

PloS one
This paper constructed a stacked-autoencoder neural network model (SAE model) to estimate sea state bias (SSB) based on radar altimeter data. Six cycles of the geophysical data record (GDR) from Jason-1/2 radar altimeters were used as a training data...