scholarly journals Proof of Feasibility of the Sea State Monitoring from Data Collected in Medium Pulse Mode by a X-Band Wave Radar System

2018 ◽  
Vol 10 (3) ◽  
pp. 459 ◽  
Author(s):  
Giovanni Ludeno ◽  
Francesco Raffa ◽  
Francesco Soldovieri ◽  
Francesco Serafino
Keyword(s):  
Pulse Mode ◽  
Radar System ◽  
State Monitoring ◽  
Sea State ◽  
X Band ◽  
Wave Radar

REMOCEAN: A Flexible X-Band Radar System for Sea-State Monitoring and Surface Current Estimation

2012 ◽  
Vol 9 (5) ◽  
pp. 822-826 ◽  
Author(s):  
F. Serafino ◽  
C. Lugni ◽  
G. Ludeno ◽  
D. Arturi ◽  
M. Uttieri ◽  
...  
Keyword(s):  
Surface Current ◽  
Radar System ◽  
State Monitoring ◽  
Sea State ◽  
Current Estimation ◽  
X Band

Sea-State Monitoring Via X-Band Marine Radar Images Sequences: A New Approach for an Accurate Surface Currents Estimation

2009 ◽  
Author(s):  
Francesco Serafino ◽  
Claudio Lugni ◽  
Francesco Soldovieri
Keyword(s):  
Synthetic Data ◽  
Surface Current ◽  
Key Factors ◽  
State Monitoring ◽  
Sea State ◽  
Reconstruction Procedure ◽  
Factors Affecting ◽  
Radar Images ◽  
X Band ◽  
Marine Radar

This work deals with the sea state monitoring starting from marine radar images collected on a moving ship. For such a topic, one of the key factors affecting the reliability of the reconstruction procedure is the determination of the equivalent surface current that also accounts for the speed of the moving ship. Here, we propose a method able to evaluate also high values of the sea surface current. The reliability of the proposed procedure is shown by a numerical analysis with synthetic data. Finally, we present some preliminary results with measurements collected on a moving ship.

scholarly journals Application of X-Band Wave Radar for Coastal Dynamic Analysis: Case Test of Bagnara Calabra (South Tyrrhenian Sea, Italy)

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Michele Punzo ◽  
Chiara Lanciano ◽  
Daniela Tarallo ◽  
Francesco Bianco ◽  
Giuseppe Cavuoto ◽  
...  
Keyword(s):  
Surf Zone ◽  
Morphological Changes ◽  
Numerical Models ◽  
Wave Number ◽  
Spatial And Temporal Variation ◽  
Tyrrhenian Sea ◽  
Sea State ◽  
Littoral Cell ◽  
X Band ◽  
Wave Radar

Sea state knowledge has a key role in evaluation of coastal erosion, the assessment of vulnerability and potential in coastal zone utilization, and development of numerical models to predict its evolution. X-band radar measurements were conducted to observe the spatial and temporal variation of the sea-state parameters along a 3 km long sandy-gravelly pocket beaches forming a littoral cell on Bagnara Calabra. We produced a sequence of 1000 images of the sea state extending offshore up to 1 mile. The survey has allowed monitoring the coastline, the directional wave spectra, the sea surface current fields, and the significant wave heights and detecting strong rip currents which cause scours around the open inlets and affect the stability of the submerged reef-type breakwaters. The possibility to validate the data acquired with other datasets (e.g., LaMMA Consortium) demonstrates the potential of the X-band radar technology as a monitoring tool to advance the understanding of the linkages between sea conditions, nearshore sediment dynamics, and coastal change. This work proves the possibility to obtain relevant information (e.g., wave number, period, and direction) for evaluation of local erosion phenomena and of morphological changes in the nearshore and surf zone.

scholarly journals X-Band Radar for the Monitoring of Sea Waves and Currents: A Comparison between Medium and Short Radar Pulses

2017 ◽  
Author(s):  
Giovanni Ludeno ◽  
Francesco Raffa ◽  
Francesco Soldovieri ◽  
Francesco Serafino
Keyword(s):  
Surface Current ◽  
Parameters Estimation ◽  
Sea Waves ◽  
State Monitoring ◽  
Sea State ◽  
Experimental Campaign ◽  
X Band ◽  
Waves And Currents ◽  
Marine Radar ◽  
Good Agreement

Abstract. This letter presents the monitoring results of the sea waves and the surface currents obtained by analyzing data acquired by a X-band marine radar in two different operative conditions, namely the short and medium pulse modes. In particular, we investigated the feasibility to use a medium radar pulse for sea state monitoring by comparing the performance in both the radar modes. The comparison was carried out by means of an experimental campaign and we observed a good agreement for surface current and sea state parameters estimation.

scholarly journals Integration between X-Band Radar and Buoy Sea State Monitoring

2016 ◽  
Author(s):  
Giovanni Ludeno ◽  
Ferdinando Reale ◽  
Francesco Raffa ◽  
Fabio Dentale ◽  
Francesco Soldovieri ◽  
...  
Keyword(s):  
Radar Data ◽  
Small Scale ◽  
State Monitoring ◽  
Sea State ◽  
Simultaneous Acquisition ◽  
X Band ◽  
Definite Improvement ◽  
Good Consistency ◽  
Monitoring Activity ◽  
Important Progress

Abstract. The paper presents the results of an integrated buoy and X-Band radar sea state monitoring activity carried out on the southern coast of Sicily. The work involved the integration of buoy and radar data, as well as the simultaneous acquisition of Significant Wave Height (SWH) values from two similar radar sets located at a slight distance from each other – a rare and fortunate circumstance which took place during two storms in the winter 2014–2015. Good consistency and repeatability was reached between the two radars and the reliability of X-Band radar as a wave monitoring system was confirmed by the comparison with the buoy wave meter. A further and important result of the work is the knowledge gained on the short spatial and temporal fluctuations of the sea state: while such Small Scale Storm Variations (SSSV) cannot be easily discriminated from electromagnetic effects and from algorithm artefacts, some important progress has been done towards the identification of this phenomenon. Integration of different sensors is the key to a definite improvement of sea state monitoring for most coastal applications.

Integration between X-Band Radar and Buoy Sea State Monitoring

2018 ◽  
Vol 34 (6) ◽  
pp. 1358
Author(s):  
Ferdinando Reale ◽  
Giovanni Ludeno ◽  
Francesco Raffa
Keyword(s):  
State Monitoring ◽  
Sea State ◽  
X Band

scholarly journals Sea surface topography reconstruction from X-band radar images

2008 ◽  
Vol 19 ◽  
pp. 83-86 ◽  
Author(s):  
F. Serafino ◽  
C. Lugni ◽  
F. Soldovieri
Keyword(s):  
Sea Surface ◽  
State Monitoring ◽  
Sea State ◽  
Radar Images ◽  
Sea Surface Topography ◽  
X Band ◽  
Sea Current ◽  
Global Vision ◽  
Inversion Procedure

Abstract. The paper deals with the feasibility study of the sea state monitoring starting from X-band radar images. The exploitation of radar images allows to achieve a global vision of the sea state compared to the local vision given by the usual sensors as the buoys. The processing approach is based on the formulation of problem as an inverse one where starting from the electromagnetic field backscattered by the sea surface, the information about the sea state are retrieved. The reliability of the inversion procedure is shown by processing synthetic and experimental data where particular attention is focussed to the determination of the sea current and speed of the vessel.

Separation of Incident and Reflected Waves by Means of a Wave Radar System

2021 ◽  
pp. 1-5
Author(s):  
Francesco Serafino ◽  
Simone Bonamano ◽  
Francesco Paladini de Mendoza ◽  
Marco Marcelli
Keyword(s):  
Radar System ◽  
Reflected Waves ◽  
Wave Radar

scholarly journals Simultaneous Target Classification and Moving Direction Estimation in Millimeter-Wave Radar System

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5228
Author(s):  
Jin-Cheol Kim ◽  
Hwi-Gu Jeong ◽  
Seongwook Lee
Keyword(s):  
Millimeter Wave ◽  
Radar System ◽  
Sensor Data ◽  
Test Field ◽  
Target Classification ◽  
Radar Sensor ◽  
Millimeter Wave Radar ◽  
Direction Estimation ◽  
Wave Radar ◽  
Moving Direction

In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training.

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