In this paper, we present a probabilistic approach which uses nadir-looking wide-band radar to detect oil spills on rough ocean surface. The proposed approach combines a single-layer scattering model with Bayesian statistics to evaluate the probability of detection of oil slicks, within a plausible range of thicknesses, on seawater. The difference between several derived detection algorithms is defined in terms of the number of frequencies used (within C-to-X-band ranges), as well as of the number of radar observations. Performance analysis of all three types of detectors (single-, dual- and tri-frequency) is done under different surface-roughness scenarios. Results show that the probability of detecting an oil slick with a given thickness is sensitive to the radar frequency. Multi-frequency detectors prove their ability to overcome the performance of the single- and dual-frequency detectors. Higher probability of detection is obtained when using multiple observations. The roughness of the ocean surface leads to a loss in the reflectivity values, and therefore decreases the performance of the detectors. A possible way to make use of the drone systems in the contingency planning is also presented.

Lebanon witnessed one of its biggest dangerous environmental crisis due to the oil spill caused by Israel bombard of fuel storage tanks in war 2006. Official reports stated that around 15000 tons of heavy fuel oil spilled in sea water. The spill threatened the maritime and human life and caused severe environmental disasters. On an international scale, the European Space Agency (ESA) stated that more than 4.5 million tons of oil is the estimate of the annual spill worldwide. Therefore, it is really crucial to have an oil spill detection system that is able to specify the spill area and to provide statistical information about the situation. One of the main techniques used nowadays is airborne and satellite remote sensing systems. The European Maritime Safety Agency (EMSA) lunched in 2016 the need to use a complementary system to the present satellite maritime surveillance system. In our work, we propose the use of drones for oil spill detection. In this paper, we present a multi-frequency technique in order to detect thick oil spill in ocean. This technique is also capable of providing an estimation of the oil thickness from reflectivity measurements affected by ocean roughness and noise in electronic devices. Numerical simulations are calculated using High Frequency Electromagnetic Field Simulation (HFSS) validating the theoretical calculations.

The need for oil spill monitoring systems has long been of concern in an attempt to contain damage with a rapid response time. When it comes to oil thickness estimation, few reliable methods capable of accurately measuring the thickness of thick oil slick (in mm) on top of the sea surface have been advanced. In this article, we provide accurate estimates of oil slick thicknesses using nadir-looking wide-band radar sensors by incorporating both C- and X-frequency bands operating over calm ocean when the weather conditions are suitable for cleaning operations and the wind speed is very low (<3 m/s). We develop Maximum-Likelihood dual- and multi-frequency statistical signal processing algorithms to estimate the thicknesses of spilled oil. The estimators use Minimum-Euclidean-Distance classification problem, in pre-defined multidimensional constellation sets, on radar reflectivity values. Furthermore, to be able to use the algorithms in oil-spill scenarios, we devise and assess the accuracy of a practical iterative procedure to use the proposed 2D and 3D estimators for accurate and reliable thickness estimations in oil-spill scenarios under noisy conditions. Results on simulated and in-lab experimental data show that M-Scan 4D estimators outperform lower-order estimators even when the iterative procedure is applied. This work is a proof that using radar measurements taken from nadir-looking systems, thick oil slick thicknesses up to 10 mm can be accurately estimated. To the best of our knowledge, the radar active sensor has not yet been used to estimate the oil slick thickness.

Oil spills are regrettably common and have socioeconomic implications on communities and disastrous consequences on the marine ecosystem and maritime life. The European Space Agency (ESA) has stated that worldwide spillage exceeds 4.5 million tons of oil annually, where 45% of the amount is due to operative discharges from ships. To alleviate the severity of oil spills and promptly react to such incidents, it is crucial to have oil-spill monitoring systems, which enable an effective contingency plan to dictate the best actions for dealing with oil spills. A quick and efficient intervention requires the (1) detection of oil slicks, (2) thickness estimation, and (3) oil classification. The European Maritime Safety Agency (EMSA) highlighted in 2016 the need to use drones as complementary systems supporting satellite maritime surveillance. While multiple sensors could be used, active radars appear to be prominent for oil spill monitoring. In this chapter, we present recent advances in drone-based radar remote sensing as an effective oil spill monitoring system. It shows from the system-level perspective the capability of radar systems on drones, using high spectral resolution and parallel scanning, to perform the above-required functionalities (1, 2, and 3) and provide valuable information to contain the damage.

In the field of communication, autoencoder (AE) refers to a system that replaces parts of the traditional transmitter and receiver with artificial neural networks (ANNs). To meet the system performance requirements, it is necessary for the AE to adapt to the changing wireless-channel conditions at runtime. Thus, online fine-tuning in the form of ANN-retraining is of great importance. Many algorithms on the ANN layer are developed to improve the AE’s performance at the communication layer. Yet, the link of the system performance and the ANN topology to the hardware layer is not fully explored. In this paper, we analyze the relations between the design layers and present a hardware implementation of an AE-based demapper that enables fine-tuning to adapt to varying channel conditions. As a platform, we selected field-programmable gate arrays (FPGAs) which provide high flexibility and allow to satisfy the low-power and low-latency requirements of embedded communication systems. Furthermore, our cross-layer approach leverages the flexibility of FPGAs to dynamically adapt the degree of parallelism (DOP) to satisfy the system-level requirements and to ensure environmental adaptation. Our solution achieves 2000× higher throughput than a high-performance graphics processor unit (GPU), draws 5× less power than an embedded central processing unit (CPU) and is 5800× more energy efficient compared to an embedded GPU for small batch size. To the best of our knowledge, such a cross-layer design approach combined with FPGA implementation is unprecedented.

Oil spills are one of the most harmful maritime disasters known to man. It is important to promptly react to spill accidents for early detection and monitoring. Improving the effectiveness of monitoring techniques for oil spills helps mitigate their environmental damage to the ecosystem. In this work, we present a new edge-based approach for the accurate detection and thickness estimation (DATE) of thick oil slicks within the range of 1 to 10 mm. The DATE approach is based on U-net models that are designed to process multiple C- and X-band radar backscattering from drones under different ocean conditions. The models are trained on synthetically generated oil spill scenarios based on fluid-dynamic and Monte-Carlo simulations. Simulation results show a high probability of detection that exceeds 90% for all possible thicknesses even at low wind speeds, which is not the case when using state-of-the-art satellite-based synthetic aperture radar techniques whose performance significantly degrades at low wind speeds. For the thickness estimation, in terms of the IoU metric, our results significantly outperform state-of-the-art solutions by a factor of 2 under varying wind speeds between 2 and 8 m/s. Moreover, we implement the new models on a hardware compute platform to verify the feasibility of edge computing on platforms like drones for effective monitoring and tactical response. Implementation results show a maximum power consumption of 1 W that suits the limited power budget of a drone, and a short latency of less than 3 s to generate the DATE maps.