RESUMEN
A Geo-Stationary GNSS-based Bistatic Forward-Looking Synthetic Aperture Radar (GeoSta-GNSS-BFLSAR) system is a particular kind of passive bistatic SAR system. In this system, a geo-stationary GNSS is used as the transmitter, while the receiver is deployed on a moving aircraft, which travels towards a target in a straight line. It is expected that such a radar system has potential for self-landing, self-navigation and battlefield information acquisition applications, etc. Up to now, little information from a research perspective can be found about GeoSta-GNSS-BFLSAR systems. To address this information gap, this paper proposes a preliminary image formation algorithm for GeoSta-GNSS-BFLSAR. The full details of the mathematical derivation are given. It is highlighted that, to overcome the long dwell time and spatial variance of GeoSta-GNSS-BFLSAR, a modified migration correction factor must be designed. In addition, the system performances and technical limitations of GeoSta-GNSS-BFLSAR such as focusing depth and spatial resolution are analytically discussed. In the end, a set of simulations including the image formation algorithm, focusing depth and spatial resolution were conducted for verification. It is demonstrated that the focusing performances of the proposed algorithm have a high level of similarity with the theoretical counterparts. This article thus proves the feasibility of GeoSta-GNSS-BFLSAR systems from a simulation level and establishes a foundation for the real applications of such a radar scheme in the future.
RESUMEN
An algorithm correcting distortion based on estimating the pixel shift is proposed for the degradation caused by underwater turbulence. The distorted image is restored and reconstructed by reference frame selection and two-dimensional pixel registration. A support vector machine-based kernel correlation filtering algorithm is proposed and applied to improve the speed and efficiency of the correction algorithm. In order to validate the algorithm, laboratory experiments on a controlled simulation system of turbulent water and field experiments in rivers and oceans are carried out, and the experimental results are compared with traditional, theoretical model-based and particle image velocimetry-based restoration and reconstruction algorithms. Using subjective visual evaluation, image distortion has been effectively suppressed; based on an objective performance statistical analysis, the measured values are better than the traditional and formerly studied restoration and reconstruction algorithms. The method proposed in this paper is also much faster than the other algorithms. It can be concluded that the proposed algorithm can effectively improve the de-distortion effect of the underwater turbulence degraded image, and provide potential techniques for the accurate operation of underwater target detection in real time.
RESUMEN
The South China Sea (SCS) sustains and is a regional center of high marine and coastal biodiversity. It is also one of the most important mariculture and marine fisheries regions in the world. Many non-indigenous species (NIS) were introduced into the SCS as artifacts of increasing mariculture production and fishery harvests. Little information exists about NIS in the SCS. In this study, research examining NIS and their threats in the SCS are reviewed. Current NIS conditions assessed include their status, threat to native biodiversity, contribution to mariculture and fisheries harvest, management, and the need for future research in specific areas are identified. A total of 90 NIS including 17 algae, 6 vascular plants, 3 bryozoans, 23 molluscs, 6 crustacea, 3 ascidians, and 32 fishes were introduced into the SCS from 1600 to the present. The primary pathways of introduction are through aquaculture, followed by shipping, ecological restoration, and biocontrol. The main introduced country is China. Some NIS have caused negative impacts on the environment and economy. Some NIS are potential threats to humans as well as biodiversity in the SCS. More research focused upon monitoring and managing NIS in the SCS is needed.