scholarly journals Weak Target Detection within the Nonhomogeneous Ionospheric Clutter Background of HFSWR Based on STAP

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Zhang ◽  
Qiang Yang ◽  
Weibo Deng
Keyword(s):  
Target Detection ◽  
High Frequency ◽  
Training Data ◽  
Data Set ◽  
Radar Echoes ◽  
Wave Radar ◽  
New Strategy ◽  
Ocean Environment ◽  
Training Data Selection ◽  
Weak Target

High Frequency Surface Wave Radar (HFSWR) can perform the functions of ocean environment monitoring, target detection, and target tracking over the horizon. However, its system's performance is always limited by the severe ionospheric clutter environment, especially by the nonhomogeneous component. The nonhomogeneous ionospheric clutter generally can cover a few Doppler shift units and a few angle units. Consequently, weak targets masked by the nonhomogeneous ionospheric clutter are difficult to be detected. In this paper, a novel algorithm based on angle-Doppler joint eigenvector which considers the angle-Doppler map of radar echoes is adopted to analyze the characteristics of the nonhomogeneous ionospheric clutter. Given the measured data set, we first investigate the correlation between the signal of interest (SOI) and the nonhomogeneous ionospheric clutter and then the correlation between the nonhomogeneous ionospheric clutters in different two ranges. Finally, a new strategy of training data selection is proposed to improve the joint domain localised (JDL) algorithm. Simulation results show that the improved-JDL algorithm is effective and the performance of weak target detection within nonhomogeneous ionospheric clutter is improved.

Target detection and tracking with a high frequency ground wave radar

1994 ◽  
Vol 19 (4) ◽  
pp. 540-548 ◽  
Author(s):  
R. Khan ◽  
B. Gamberg ◽  
D. Power ◽  
J. Walsh ◽  
B. Dawe ◽  
...  
Keyword(s):  
Target Detection ◽  
High Frequency ◽  
Detection And Tracking ◽  
Wave Radar ◽  
Ground Wave

scholarly journals Postoperative seizure outcome-guided machine learning for interictal electrocorticography in neocortical epilepsy

2018 ◽  
Vol 119 (6) ◽  
pp. 2265-2275 ◽  
Author(s):  
Seong-Cheol Park ◽  
Chun Kee Chung
Keyword(s):  
Machine Learning ◽  
Free Group ◽  
High Frequency ◽  
Low Frequency ◽  
Outcome Data ◽  
Training Data ◽  
Seizure Outcome ◽  
Data Set ◽  
Neocortical Epilepsy ◽  
Postoperative Seizure

The objective of this study was to introduce a new machine learning guided by outcome of resective epilepsy surgery defined as the presence/absence of seizures to improve data mining for interictal pathological activities in neocortical epilepsy. Electrocorticographies for 39 patients with medically intractable neocortical epilepsy were analyzed. We separately analyzed 38 frequencies from 0.9 to 800 Hz including both high-frequency activities and low-frequency activities to select bands related to seizure outcome. An automatic detector using amplitude-duration-number thresholds was used. Interictal electrocorticography data sets of 8 min for each patient were selected. In the first training data set of 20 patients, the automatic detector was optimized to best differentiate the seizure-free group from not-seizure-free-group based on ranks of resection percentages of activities detected using a genetic algorithm. The optimization was validated in a different data set of 19 patients. There were 16 (41%) seizure-free patients. The mean follow-up duration was 21 ± 11 mo (range, 13–44 mo). After validation, frequencies significantly related to seizure outcome were 5.8, 8.4–25, 30, 36, 52, and 75 among low-frequency activities and 108 and 800 Hz among high-frequency activities. Resection for 5.8, 8.4–25, 108, and 800 Hz activities consistently improved seizure outcome. Resection effects of 17–36, 52, and 75 Hz activities on seizure outcome were variable according to thresholds. We developed and validated an automated detector for monitoring interictal pathological and inhibitory/physiological activities in neocortical epilepsy using a data-driven approach through outcome-guided machine learning. NEW & NOTEWORTHY Outcome-guided machine learning based on seizure outcome was used to improve detections for interictal electrocorticographic low- and high-frequency activities. This method resulted in better separation of seizure outcome groups than others reported in the literature. The automatic detector can be trained without human intervention and no prior information. It is based only on objective seizure outcome data without relying on an expert’s manual annotations. Using the method, we could find and characterize pathological and inhibitory activities.

scholarly journals Adaptation and Selection Techniques Based on Deep Learning for Human Activity Recognition Using Inertial Sensors

2020 ◽  
Vol 2 (1) ◽  
pp. 22
Author(s):  
Manuel Gil-Martín ◽  
José Antúnez-Durango ◽  
Rubén San-Segundo
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Inertial Sensors ◽  
Human Activity Recognition ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Data Set ◽  
Physical Activity Monitoring ◽  
Training Data Selection

Deep learning techniques have been widely applied to Human Activity Recognition (HAR), but a specific challenge appears when HAR systems are trained and tested with different subjects. This paper describes and evaluates several techniques based on deep learning algorithms for adapting and selecting the training data used to generate a HAR system using accelerometer recordings. This paper proposes two alternatives: autoencoders and Generative Adversarial Networks (GANs). Both alternatives are based on deep neural networks including convolutional layers for feature extraction and fully-connected layers for classification. Fast Fourier Transform (FFT) is used as a transformation of acceleration data to provide an appropriate input data to the deep neural network. This study has used acceleration recordings from hand, chest and ankle sensors included in the Physical Activity Monitoring Data Set (PAMAP2) dataset. This is a public dataset including recordings from nine subjects while performing 12 activities such as walking, running, sitting, ascending stairs, or ironing. The evaluation has been performed using a Leave-One-Subject-Out (LOSO) cross-validation: all recordings from a subject are used as testing subset and recordings from the rest of the subjects are used as training subset. The obtained results suggest that strategies using autoencoders to adapt training data to testing data improve some users’ performance. Moreover, training data selection algorithms with autoencoders provide significant improvements. The GAN approach, using the generator or discriminator module, also provides improvement in selection experiments.

Reliable data-driven modeling of high-frequency structures by means of nested kriging with enhanced design of experiments

2019 ◽  
Vol 36 (7) ◽  
pp. 2293-2308 ◽  
Author(s):  
Slawomir Koziel ◽  
Anna Pietrenko-Dabrowska
Keyword(s):  
Design Of Experiments ◽  
Parameter Space ◽  
Parameter Optimization ◽  
High Frequency ◽  
Computational Cost ◽  
Small Region ◽  
Training Data ◽  
Data Set ◽  
Content Type ◽  
Modeling Process

Purpose A framework for reliable modeling of high-frequency structures by nested kriging with an improved sampling procedure is developed and extensively validated. A comprehensive benchmarking including conventional kriging and previously reported design of experiments technique is provided. The proposed technique is also demonstrated in solving parameter optimization task. Design/methodology/approach The keystone of the proposed approach is to focus the modeling process on a small region of the parameter space (constrained domain containing high-quality designs with respect to the selected performance figures) instead of adopting traditional, hyper-cube-like domain defined by the lower and upper parameter bounds. A specific geometry of the domain is explored to improve a uniformity of the training data set. In consequence, the predictive power of the model is improved. Findings Building the model in a constrained domain allows for a considerable reduction of a training data set size without a necessity to either narrow down the parameter ranges or to reduce the parameter space dimensionality. Improving uniformity of training data set allocation permits further reduction of the computational cost of setting up the model. The proposed technique can be used to expedite the parameter optimization and enables locating good initial designs in a straightforward manner. Research limitations/implications The developed framework opens new possibilities inaccurate surrogate modeling of high-frequency structures described by a large number of geometry and/or material parameters. Further extensions can be investigated such as the inclusion of the sensitivity data into the model or exploration of the particular geometry of the model domain to further reduce the computational overhead of training data acquisition. Originality/value The efficiency of the proposed method has been demonstrated for modeling and parameter optimization of high-frequency structures. It has also been shown to outperform conventional kriging and previous constrained modeling approaches. To the authors’ knowledge, this approach to formulate and handle the modeling process is novel and permits the establishment of accurate surrogates in highly dimensional spaces and covering wide ranges of parameters.

Vessel target detection using zero‐Doppler spectra of radar echo for high‐frequency surface wave radar

2016 ◽  
Vol 10 (7) ◽  
pp. 1243-1248 ◽  
Author(s):  
Yonggang Ji ◽  
Jie Zhan ◽  
Yiming Wang ◽  
Xiaoliang Chu ◽  
Ming Li
Keyword(s):  
Surface Wave ◽  
Target Detection ◽  
High Frequency ◽  
Wave Radar ◽  
Radar Echo
Sign in / Sign up

Export Citation Format

Share Document