Results 31 to 40 of about 3,347 (296)

Sea Clutter Spectral Parameters Prediction and Influence Factor Analysis Based on Deep Learning

Leida xuebao, 2023
Using Deep Neural Network (DNN) modeling technology, a prediction model of Doppler spectral parameters of sea clutter based on multiple measurement conditions is established based on measured data of sea clutter from shore-based radar under different ...
Yushi ZHANG, Xiaoyu LI, Jinpeng ZHANG, Xiaoyun XIA   +3 more
doaj   +1 more source

Overview and Prospects of Radar Sea Clutter Measurement Experiments

Leida xuebao, 2019
In complex marine environments, sea clutter greatly affects the detection performance of maritime targets. Because the influencing factors of sea clutter are numerous and the mechanism is complex, there are great difficulties in feature description and ...
DING Hao, LIU Ningbo, DONG Yunlong, CHEN Xiaolong, GUAN Jian   +4 more
doaj   +1 more source

Bi-Phase Compound-Gaussian Mixture Model of Sea Clutter and Scene-Segmentation-Based Target Detection

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
In high-resolution maritime surveillance radars, sea clutter exhibits highly spatial heterogeneity due to modulation of long waves with wavelengths longer than the width of one range cell.
Xiang Liang, Peng-Lang Shui, Hong-Tao Su
doaj   +1 more source

Statistical analysis of bistatic and monostatic sea clutter

IEEE Transactions on Aerospace and Electronic Systems, 2015
In this work, we analyze the sea clutter data collected simultaneously by the bistatic and monostatic nodes of an S-band netted radar system under different out-of-plane geometries. The empirical distribution of sea clutter data are fitted to some known heavy-tailed distributions, and the sea spikes statistics are examined.
PALAMA', RICCARDO, GRECO, MARIA, STINCO, PIETRO, GINI, FULVIO   +3 more
openaire   +2 more sources

Sea Clutter Suppression Method Based on Ocean Dynamics Using the WRF Model

Journal of Marine Science and Engineering
Sea clutter introduces a significant amount of non-target reflections in the echo signals received by radar, complicating target detection and identification.
Guigeng Li, Zhaoqiang Wei, Yujie Chen, Xiaoxia Meng, Hao Zhang   +4 more
doaj   +1 more source

Prediction of Sea Surface Reflectivity under Different Sea Conditions Based on the Clustering of Marine Environmental Parameters

Remote Sensing, 2023
The high-precision prediction of sea clutter reflectivity is helpful in improving the performance of marine radar and sea surface remote sensing capabilities.
Yalan Li, Liwen Ma, Yushi Zhang, Tao Wu, Jinpeng Zhang, Haiying Li   +5 more
doaj   +1 more source

Sea Clutter Suppression Based on Chaotic Prediction Model by Combining the Generator and Long Short-Term Memory Networks

Remote Sensing
Sea clutter usually greatly affects the target detection and identification performance of marine surveillance radars. In order to reduce the impact of sea clutter, a novel sea clutter suppression method based on chaos prediction is proposed in this ...
Jindong Yu, Baojing Pan, Ze Yu, Hongling Zhu, Hanfu Li, Chao Li, Hezhi Sun   +6 more
doaj   +1 more source

The Doppler Spectra of Medium Grazing Angle Sea Clutter; Part 1: Characterisation [PDF]

, 2016
This paper is concerned with the characterisation of Doppler spectra from high range resolution X-band radar sea clutter observed from an airborne platform over the range of grazing angles, 15° to 45°.
Rosenberg, L, Ritchie, MA, Watts, S, Bocquet, S   +3 more
core  

Sea Clutter Amplitude Statistics Analysis by Goodness-of-Fit Test [PDF]

, 2012
This paper provides a quantitative method for analyzing the amplitudes statistics of sea clutter by goodness-of-fit (GOF) tests. It first introduces several well-known GOF tests, and then employs them to test the amplitude statistics of recorded live sea
Gao, Chao, Yang, Jian, Deng, Xiaobo
core   +1 more source

Soft Skins With Reversible Thickness Morphing: Materials, Mechanisms, and Applications

Advanced Materials, EarlyView.
Evolution of electronic skin (e‐skin) technologies toward adaptive, multifunctional soft skins. Phase I highlights early rigid and discrete sensory interfaces. Phase II shows the transition toward flexible, stretchable, and large‐area e‐skin. Phase III captures the emergence of computational e‐skin.
Oliver Ozioko, Chiamaka Akah, Ravinder Dahiya   +2 more
wiley   +1 more source