Analysis of Small Sea-Surface Targets Detection Performance According to Airborne Radar Parameters in Abnormal Weather Environments

Bounaceur, Hamza; Khenchaf, Ali; Caillec, Jean-Marc Le

doi:10.3390/s22093263

Cited by 7 publications

(3 citation statements)

References 39 publications

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“…It is expected that the use of drones will increase by an order of magnitude over coming years, so more robust surveillance methods are required [3]. Radar is an emerging technology for the use of detection, tracking and classification of S-UAS [4][5][6] and has the capability to operate during night and in all weather conditions [7,8]. One particular class of modern radars, active electronically scanned arrays (AESA) utilise a grid of transceiver antennas with adjustable phase shifters [9] to swiftly change the pointing direction of the beam and this allows for more sophisticated signal processing techniques to improve the radar's effectiveness [10].…”

Section: Introductionmentioning

confidence: 99%

Fully digital, urban networked staring radar: Simulation and experimentation

Griffiths,

Jahangir,

Kannanthara

et al. 2023

IET Radar Sonar & Navi

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The application and uses of drones in all areas are continuously rising, especially in civilian use cases. This increasing threat requires reliable drone surveillance in urban environments. Radar is the obvious candidate with its ability to detect small objects at range, in all weather conditions. The use of an L‐band networked radar for urban sensing of S‐UAS targets is explored. Small echoes from S‐UAS places a premium on synchronisation, which is the fundamental key for high performance networked radar. The effect of timing errors on the operation of the network radar is investigated theoretically and experimentally, and the processing tools for synchronising data based on the direct signal returns of the transmitter are developed. Also, drone detection using bistatic L‐band staring radar is achieved both in simulation and then in real field trials where the SNR and detection performance are computed and analysed. The updated direct signal synchronisation method for bistatic staring radar is shown to provide comparable SNR and positional accuracy for S‐UAS targets as the monostatic staring radar.

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Section: Introductionmentioning

confidence: 99%

Fully digital, urban networked staring radar: Simulation and experimentation

Griffiths,

Jahangir,

Kannanthara

et al. 2023

IET Radar Sonar & Navi

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“…We perform multi-scale sea surface modeling [11,12] and analyze sea clutter characteristics [6] in the vicinity of the data collection points to support subsequent sea surface target detection. In order to monitor sea surface targets over larger regions, it is necessary to conduct real sea surface modeling and analysis of sea clutter characteristics over a wide area [13]. Therefore, there is an urgent need to process the marine environmental data provided by ECMWF for the surrounding marine areas of China at a high resolution.…”

Section: Introductionmentioning

confidence: 99%

Research on High-Resolution Reconstruction of Marine Environmental Parameters Using Deep Learning Model

Zhang³

et al. 2023

Remote Sensing

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The analysis of marine environmental parameters plays a significant role in various aspects, including sea surface target detection, the monitoring of the marine ecological environment, marine meteorology and disaster forecasting, and the monitoring of internal waves in the ocean. In particular, for sea surface target detection, the accurate and high-resolution input of marine environmental parameters is crucial for multi-scale sea surface modeling and the prediction of sea clutter characteristics. In this paper, based on the low-resolution wind speed, significant wave height, and wave period data provided by ECMWF for the surrounding seas of China (specified latitude and longitude range), a deep learning model based on a residual structure is proposed. By introducing an attention module, the model effectively addresses the poor modeling performance of traditional methods like nearest neighbor interpolation and linear interpolation at the edge positions in the image. Experimental results demonstrate that with the proposed approach, when the spatial resolution of wind speed increases from 0.5° to 0.25°, the results achieve a mean square error (MSE) of 0.713, a peak signal-to-noise ratio (PSNR) of 49.598, and a structural similarity index measure (SSIM) of 0.981. When the spatial resolution of the significant wave height increases from 1° to 0.5°, the results achieve a MSE of 1.319, a PSNR of 46.928, and an SSIM of 0.957. When the spatial resolution of the wave period increases from 1° to 0.5°, the results achieve a MSE of 2.299, a PSNR of 44.515, and an SSIM of 0.940. The proposed method can generate high-resolution marine environmental parameter data for the surrounding seas of China at any given moment, providing data support for subsequent sea surface modeling and for the prediction of sea clutter characteristics.

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“…This interest is motivated by the need for a better understanding of sea-surface backscattering as a physical phenomenon and by its prospective application in the development and improvement of remote sensing technology. Therefore, research on sea-surface backscattering is very important to understanding the formation mechanism of sea clutter, which is crucial for radar target detection in nonhomogeneous environments [ 1 , 2 , 3 , 4 ], and for operational monitoring of waves, currents, and sea winds [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the NRCS Sampling at the Sea Wind Retrieval by the Airborne Rotating-Beam Scatterometer Mounted under Fuselage

Nekrasov

Khachaturian

Vorobev

2022

Sensors

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The optimization of normalized radar cross-section (NRCS) sampling by a scatterometer allows an increase in the accuracy of the wind retrieval over the water surface and a decrease in the time of the measurement. Here, we investigate the possibility of improving wind vector measurement with an airborne rotating-beam scatterometer mounted under the fuselage. For this purpose, we investigated NRCS sampling at various incidence angles, and the possibility of using NRCS samples obtained during simultaneous measurement at different incidence angles to perform wind retrieval. The proposed wind algorithms are based on a geophysical model function (GMF). Sea wind retrieval was carried out using Monte Carlo simulations with consideration of a single incidence angle or combinations of several incidence angles. The incidence angles of interest were 30°, 35°, 40°, 45°, 50°, 55°, and 60°. The simulation showed that the wind speed error decreased with an increase in the incidence angle, and the wind direction error tended to decrease with an increase in the incidence angle. The single incidence angle case is characterized by higher maximum wind retrieval errors but allows for a higher maximum altitude of the wind retrieval method’s applicability to be achieved. The use of several neighboring incidence angles allows a better wind vector retrieval accuracy to be achieved. The combinations of three and four incidence angles provided the lowest maximum wind speed and direction errors in the range of the incidence angles from 45° to 60° but, unfortunately, provide the lowest maximum altitude of applicability of the wind retrieval method. At the same time, the combination of two incidence angles is characterized by slightly higher maximum wind retrieval errors than in the cases of three and four incidence angles, but they are lower than in the case of the single incidence angle. Moreover, the two incidence angles’ combination is a simpler way to decrease the wind retrieval errors, especially for measurement near an incidence angle of 30°, providing nearly the highest maximum altitude of the wind retrieval method applicability. The results obtained can be used to enhance existing airborne radars and in the development of new remote sensing systems.

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Analysis of Small Sea-Surface Targets Detection Performance According to Airborne Radar Parameters in Abnormal Weather Environments

Cited by 7 publications

References 39 publications

Fully digital, urban networked staring radar: Simulation and experimentation

Fully digital, urban networked staring radar: Simulation and experimentation

Research on High-Resolution Reconstruction of Marine Environmental Parameters Using Deep Learning Model

Optimization of the NRCS Sampling at the Sea Wind Retrieval by the Airborne Rotating-Beam Scatterometer Mounted under Fuselage

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