Spectral analyses of the dual polarization Doppler weather radar data.

Echoes in clear air from biological scatterers mixed within the resolution volumes over a large region are presented. These echoes were observed with the polarimetric prototype of the forthcoming WSR-88D weather radar. The study case occurred in the evening of September 7, 2004, at the beginning of the bird migrating season. Novel polarimetric spectral analyses are used for distinguishing signatures of birds and insects in multimodal spectra. These biological scatterers were present at the same time in the radar resolution volumes over a large area. Spectral techniques for (1) data censoring, (2) wind retrieval and (3) estimation of intrinsic values/functions of polarimetric variables for different types of scatterers are presented. The technique for data censoring in the frequency domain allows detection of weak signals. Censoring is performed on the level of spectral densities, allowing exposure of contributions to the spectrum from multiple types of scatterers. The spectral techniques for wind retrieval allow simultaneous estimation of wind from the data that are severely contaminated by migrating birds, and assessment of bird migration parameters. The intrinsic polarimetric signatures associated with the variety of scatterers can be evaluated using presented methodology. Algorithms for echo classification can be built on these. The possibilities of spectral processing using parametric estimation techniques are explored for resolving contributions to the Doppler spectrum from the three types of scatterers: passive wind tracers, actively flying insects and birds. A combination of parametric and non-parametric polarimetric spectral analyses is used to estimate the small bias introduced to the wind velocity by actively flying insects

DETECTION THRESHOLDS FOR SPECTRAL MOMENTS AND POLARIMETRIC VARIABLES

Presently, the Signal-to-Noise-Ratio (SNR) measurement is used to determine the presence of a weather signal for Weather Surveillance Radar - 1988 Doppler (WSR-88D). Growing popularity of polarimetric radars prompts the need for improved signal detection scheme. Namely, the ongoing upgrade of the WSR-88D network to dual polarization results in a 3 dB reduction of the SNR per channel because the existing transmitter power is split between horizontal (H) and vertical (V) channels. Therefore, the radar sensitivity is degraded and many valid weather signals may be discarded if the current censoring scheme is retained. In this work, statistical techniques of mitigating the impact of the 3 dB SNR loss with the goal of improving data censoring for the dual-polarization system are examined. First, the performance and implementation of a classical likelihood-ratio method is investigated. It is concluded that such a method is not practical for operational systems due to insufficient processing capability of the signal processor. With the system constraint in mind, several efficient methods based on the signal coherency in sample-time and across channels, such as power and autocorrelation measurements in H and V channels, as well as the cross-correlation of signals from the H and V channels, are proposed. Statistical analyses of various combinations of these variables are performed using Monte Carlo simulations. The performance is further demonstrated and verified using time series data collected by the research polarimetric radar (KOUN), operated by the National Severe Storms Laboratory. Both the statistical analysis and the performance comparisons on time series imply that the novel approach has the potential to significantly improve the signal detection on dual-polarization weather radars; thus mitigating the impact of the 3 dB SNR loss in the WSR-88D radars

AN ARTIFICIAL INTELLIGENCE APPROACH TO THE PROCESSING OF RADAR RETURN SIGNALS FOR TARGET DETECTION

Most of the operating vessel traffic management systems experience problems, such as track loss and track swap, which may cause confusion to the traffic regulators and lead to potential hazards in the harbour operation. The reason is mainly due to the limited adaptive capabilities of the algorithms used in the detection process. The decision on whether a target is present is usually based on the magnitude of the returning echoes. Such a method has a low efficiency in discriminating between the target and clutter, especially when the signal to noise ratio is low. The performance of radar target detection depends on the features, which can be used to discriminate between clutter and targets. To have a significant improvement in the detection of weak targets, more obvious discriminating features must be identified and extracted. This research investigates conventional Constant False Alarm Rate (CFAR) algorithms and introduces the approach of applying ar1ificial intelligence methods to the target detection problems. Previous research has been unde11aken to improve the detection capability of the radar system in the heavy clutter environment and many new CFAR algorithms, which are based on amplitude information only, have been developed. This research studies these algorithms and proposes that it is feasible to design and develop an advanced target detection system that is capable of discriminating targets from clutters by learning the .different features extracted from radar returns. The approach adopted for this further work into target detection was the use of neural networks. Results presented show that such a network is able to learn particular features of specific radar return signals, e.g. rain clutter, sea clutter, target, and to decide if a target is present in a finite window of data. The work includes a study of the characteristics of radar signals and identification of the features that can be used in the process of effective detection. The use of a general purpose marine radar has allowed the collection of live signals from the Plymouth harbour for analysis, training and validation. The approach of using data from the real environment has enabled the developed detection system to be exposed to real clutter conditions that cannot be obtained when using simulated data. The performance of the neural network detection system is evaluated with further recorded data and the results obtained are compared with the conventional CFAR algorithms. It is shown that the neural system can learn the features of specific radar signals and provide a superior performance in detecting targets from clutters. Areas for further research and development arc presented; these include the use of a sophisticated recording system, high speed processors and the potential for target classification

Investigation of polarimetric measurements of rainfall at close and distant ranges.

As part of continuous modernization of operational weather radar systems, several government agencies have explored adding polarimetric capability to existing networks. Polarimetric measurements of rain have been previously shown robust with respect to drop size distribution variations, hail contamination, and offer improved echo classification capabilities. Other advantages of polarimetric measurements of rain include an immunity to radar miscalibration, attenuation in rain and partial beam blockage.This study is motivated by the lack of validation for polarimetric rainfall applications with respect to coverage and accuracy demands of the operational weather community. To address these concerns, the quality of polarimetric measurements and rainfall estimation is investigated over a broad range of distances. Several new methods to identify regions of known radar bias and improve radar rainfall measurements in operations are provided.Whereas the existing literature overwhelmingly recommends polarimetric methods for weather radar applications, the majority of dual-polarization studies have been conducted on datasets collected at relatively close distance to the radar. However, it is well-known that the quality of radar measurements and rainfall estimates degrades with distance due to factors including beam broadening, the effect of Earth curvature and the overshooting of precipitation

The InflateSAR Campaign: Evaluating SAR Identification Capabilities of Distressed Refugee Boats

Most of the recent research in the field of marine target detection has been concentrating on ships with large metallic parts. The focus of this work is on much more challenging targets represented by small rubber inflatables. They are of importance, since in recent years they have largely been used by migrants to cross the Mediterranean Sea between Libya and Europe. The motivation of this research is to mitigate the ongoing humanitarian crisis at Europe’s southern borders. These boats, packed with up to 200 people, are in no way suitable to cross the Mediterranean Sea or any other big water body and are in distress from the moment of departure. The establishment of a satellite-based surveillance infrastructure could considerably support search and rescue missions in the Mediterranean Sea, reduce the number of such boats being missed and mitigate the ongoing death in the open ocean. In this work we describe and analyze data from the InflateSAR acquisition campaign, wherein we gathered multiple-platform SAR imagery of an original refugee inflatable. The test site for this campaign is a lake which provides background clutter that is more predictable. The analysis considered a sum of experiments, enabling investigations of a broad range of scene settings, such as the vessel’s orientation, superstructures and speed. We assess their impact on the detectability of the chosen target under different sensor parameters, such as polarimetry, resolution and incidence angle. Results show that TerraSAR-X Spotlight and Stripmap modes offer good capabilities to potentially detect those types of boats in distress. Low incidence angles and cross-polarization decrease the chance of a successful identification, whereas a fully occupied inflatable, orthogonally oriented to the line of sight, seems to be better visible than an empty one. The polarimetric analyses prove the vessel’s different polarimetric behavior in comparison with the water surface, especially when it comes to entropy. The analysis considered state-of-the-art methodologies with single polarization and dual polarization channels. Finally, different metrics are used to discuss whether and to which extent the results are applicable to other open ocean datasets. This paper does not introduce any vessel detection or classification algorithm from SAR images. Rather, its results aim at paving the way to the design and the development of a specially tailored detection algorithm for small rubber inflatables

Earth Resources: A continuing bibliography with indexes, issue 16, January 1978

This bibliography lists 543 reports, articles, and other documents introduced onto the NASA scientific and technical information system between October 1 and December 31, 1977. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Imagining Earth: Concepts of Wholeness in Cultural Constructions of Our Home Planet

While concepts of Earth have a rich tradition, more recent examples show a distinct quality: Though ideas of wholeness might still be related to mythical, religious, or utopian visions of the past, "Earth" itself has become available as a whole. This raises several questions: How are the notions of one Earth or our Planet imagined and distributed? What is the role of cultural imagination and practices of signification in the imagination of "the Earth"? Which theoretical models can be used or need to be developed to describe processes of imagining Planet Earth? This collection invites a wide range of perspectives from different fields of the Humanities to explore the means of imagining Earth