A High-Frequency Surface Wave Radar Simulation Using FMCW Technique for Ship Detection

Indonesia is an archipelagic country with a vast sea area. This vast sea area becomes a challenge in conducting regional surveillance to maintain maritime conditions. The use of buoys and satellites still has shortcomings in carrying out surveillance despite its excellent surveillance capabilities. A high-frequency radar technology with 3-30 MHz frequency and surface wave propagation are very suitable because it has a radar range that can cross the horizon or commonly refer to as Over the Horizon (OTH). The Frequency Modulated Continuous Wave (FMCW) technique on this radar obtains distance and velocity information by a continuously transmitted frequency modulation. The use of radar in Indonesia for marine surveillance is still infrequent. Therefore, it is relatively difficult to conduct testing and obtain data. In addition, the direct examination requires extended time, so a simulation program is needed. This paper discusses the design of a High-Frequency Surface Wave Radar (HFSWR) simulation program using FMCW modulation technique. The simulation program detected two objects based on time delays due to the distance and velocity of the object with a maximum range of 350 km. It displayed the results in an informative manner. The object detection was based on the results of the Fast Fourier Transform (FFT) from the mixed signals. The mixed signal is a combination of transmitted signal and reflected signal in which there are time delay components due to the object. The simulation program had been tested with input values of distance and velocity that vary, both for one object and two objects, in the radial direction. It generated output that was close to the input value with a level of accuracy of ± 2 km

Electromagnetic backscatter modelling of icebergs at c-band in an ocean environment

This thesis outlines the development of an electromagnetic (EM) backscatter model of icebergs. It is a necessary first step for the generation of in-house synthetic aperture radar (SAR) data of icebergs to support optimum iceberg/ship classifier design. The EM modelling was developed in three stages. At first, an EM backscatter model was developed to generate simulated SAR data chips of iceberg targets at small incidence angles. The model parameters were set to mimic a dual polarized dataset collected at C-Band with the Sentinel-1A satellite. The simulated SAR data chips were compared with signatures and radiometric properties of the satellite data, including total radar cross section (TRCS). A second EM model was developed to mimic the parameters of a second SAR data collection with RADARSAT-2; this second data collection was at larger incidence angles and was fully polarimetric (four channels and interchannel phase). The full polarimetric SAR data allowed for a comparison of modelled TRCS and polarimetric decompositions. Finally, the EM backscatter models were tested in the context of iceberg/ship classification by comparing the performance of various computer vision classifiers using both simulated and real SAR image data of iceberg and vessel targets. This step is critical to check the compatibility of simulated data with the real data, and the ability to mix real and simulated SAR imagery for the generation of skilled classifiers. An EM backscatter modelling tool called GRECOSAR was used for the modelling work. GRECOSAR includes the ability to generate small scenes of the ocean using Pierson-Moskowitz spectral parameters. It also allows the placement of a 3D target shape into that ocean scene. Therefore, GRECOSAR is very useful for simulating SAR targets, however it can only model single layer scattering from the targets. This was found to be limiting in that EM scattering throughout volume of the iceberg could not be generated. This resulted in EM models that included only surface scattering of the iceberg. In order to generate realistic SAR scenes of icebergs on the ocean, 3D models of icebergs were captured in a series of field programs off the coast of Newfoundland and Labrador, Canada. The 3D models of the icebergs were obtained using a light detection and ranging (LiDAR) and multi-beam sonar data from a specially equipped vessel by a team of C-CORE. While profiling the iceberg targets, SAR images from satellites were captured for comparison with the simulated SAR images. The analysis of the real and simulated SAR imagery included comparisons of TRCS, SAR signature morphology and polarimetric decompositions of the targets. In general, these comparisons showed a good consistency between the simulated and real SAR scene. Simulations were also performed with varying target orientation and sea conditions (i.e., wind speed and direction). A wide variability of the TRCS and SAR signature morphology was observed with varying scene parameters. Icebergs were modelled using a high dielectric constant to mimic melting iceberg surfaces as seen during field work. Given that GRECOSAR could only generate surface backscatter, a mathematical model was developed to quantify the effect of melt water on the amount of surface and volume backscatter that might be expected from the icebergs. It was found that the icebergs in a high state of melt should produce predominantly surface scatter, thus validating the use of GRECOSAR for icebergs in this condition. Once the simulated SAR targets were validated against the real SAR data collections, a large dataset of simulated SAR chips of ships and icebergs were created specifically for the purpose of target classification. SAR chips were generated at varying imaging parameters and target sizes and passed on to an iceberg/ship classifier. Real and simulated SAR chips were combined in varying quantities (or targets) resulting in a series of different classifiers of varying skill. A good agreement between the classifier’s performance was found. This indicates the compatibility of the simulated SAR imagery with this application and provides an indication that the simulated data set captures all the necessary physical properties of icebergs for ship and iceberg classification

Estado da Arte do Sensoriamento Remoto de Radar: Fundamentos, Sensores, Processamento de Imagens e Aplicações.

Este artigo aborda o estado da arte do sensoriamento remoto por radar e foi elaborado para fazer parte da edição especial de comemoração dos 50 anos desta revista. Neste estudo, é apresentada uma breve introdução sobre os fundamentos do sensoriamento remoto por radar, com destaque para os parâmetros mais importantes de imageamento e da superfície terrestre envolvidos no processo de obtenção de imagens de radar. Ênfase é dada para o comprimento de onda, polarização das ondas eletromagnéticas e geometria de obtenção de imagens (parâmetros de imageamento) e para a umidade de solos e da vegetação, rugosidade do terreno e estrutura da vegetação (parâmetros da superfície terrestre). Em seguida, são apresentados os principais sensores orbitais de radar de abertura sintética que estão atualmente em operação e os principais processamentos digitais de imagens de radar, destacando-se a conversão dos valores digitais para coeficientes de retroespalhamento, os filtros espaciais para redução do ruído speckle, as técnicas de decomposição de imagens e o processamento InSAR. Finalmente, é apresentada uma breve discussão sobre algumas aplicações potenciais, com especial atenção para o monitoramento de derrame de óleo em plataformas continentais, estimativa de biomassa aérea, monitoramento de desmatamento em coberturas florestais tropicais, detecção de áreas de plantio de arroz irrigado e estimativa de umidade de solos

Improving Ship Detection with Polarimetric SAR based on Convolution between Co-polarization Channels

The convolution between co-polarization amplitude only data is studied to improve ship detection performance. The different statistical behaviors of ships and surrounding ocean are characterized a by two-dimensional convolution function (2D-CF) between different polarization channels. The convolution value of the ocean decreases relative to initial data, while that of ships increases. Therefore the contrast of ships to ocean is increased. The opposite variation trend of ocean and ships can distinguish the high intensity ocean clutter from ships' signatures. The new criterion can generally avoid mistaken detection by a constant false alarm rate detector. Our new ship detector is compared with other polarimetric approaches, and the results confirm the robustness of the proposed method