Distributed physical sensors network for the protection of critical infrastractures against physical attacks

The SCOUT project is based on the use of multiple innovative and low impact technologies for the protection of space control ground stations and the satellite links against physical and cyber-attacks, and for intelligent reconfiguration of the ground station network (including the ground node of the satellite link) in the case that one or more nodes fail. The SCOUT sub-system devoted to physical attacks protection, SENSNET, is presented. It is designed as a network of sensor networks that combines DAB and DVB-T based passive radar, noise radar, Ku-band radar, infrared cameras, and RFID technologies. The problem of data link architecture is addressed and the proposed solution described

Radar systems for a polar mission, volume 3, appendices A-D, S, T

Success is reported in the radar monitoring of such features of sea ice as concentration, floe size, leads and other water openings, drift, topographic features such as pressure ridges and hummocks, fractures, and a qualitative indication of age and thickness. Scatterometer measurements made north of Alaska show a good correlation with a scattering coefficient with apparent thickness as deduced from ice type analysis of stereo aerial photography. Indications are that frequencies from 9 GHz upward seem to be better for sea ice radar purposes than the information gathered at 0.4 GHz by a scatterometer. Some information indicates that 1 GHz is useful, but not as useful as higher frequencies. Either form of like-polarization can be used and it appears that cross-polarization may be more useful for thickness measurement. Resolution requirements have not been fully established, but most of the systems in use have had poorer resolution than 20 meters. The radar return from sea ice is found to be much different than that from lake ice. Methods to decrease side lobe levels of the Fresnel zone-plate processor and to decrease the memory requirements of a synthetic radar processor are discussed

Examples of current radar technology and applications, chapter 5, part B

Basic principles and tradeoff considerations for SLAR are summarized. There are two fundamental types of SLAR sensors available to the remote sensing user: real aperture and synthetic aperture. The primary difference between the two types is that a synthetic aperture system is capable of significant improvements in target resolution but requires equally significant added complexity and cost. The advantages of real aperture SLAR include long range coverage, all-weather operation, in-flight processing and image viewing, and lower cost. The fundamental limitation of the real aperture approach is target resolution. Synthetic aperture processing is the most practical approach for remote sensing problems that require resolution higher than 30 to 40 m

X-band synthetic aperture radar methods

Spaceborne Synthetic Aperture Radars (SARs), operating at L-band and above, offer microwave observations of the Earth at very high spatial resolution in almost all-weather conditions. Nevertheless, precipitating clouds can significantly affect the signal backscattered from the ground surface in both amplitude and phase, especially at X band and beyond. This evidence has been assessed by numerous recent efforts analyzing data collected by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions at X band. This sensitivity can be exploited to detect and quantify precipitations from SARs at the spatial resolution of a few hundred meters, a very appealing feature considering the current resolution of precipitation products from space. Forward models of SAR response in the presence of precipitation have been developed for analyzing SAR signature sensitivity and developing rainfall retrieval algorithms. Precipitation retrieval algorithms from SARs have also been proposed on a semi-empirical basis. This chapter will review experimental evidences, modelling approaches, retrieval methods and recent applications of X-band SAR data to rainfall estimation

The GPM GV Program

We present a detailed overview of the structure and activities associated with the NASA-led ground validation component of the NASA-JAXA Global Precipitation Measurement (GPM) mission. The overarching philosophy and approaches for NASAs GV program are presented with primary focus placed on aspects of direct validation and a summary of physical validation campaigns and results. We describe a spectrum of key instruments, methods, field campaigns and data products developed and used by NASAs GV team to verify GPM level-2 precipitation products in rain and snow. We describe the tools and analysis framework used to confirm that NASAs Level-1 science requirements for GPM are met by the GPM Core Observatory. Examples of routine validation activities related to verification of Integrated Multi-satellitE Retrievals for GPM (IMERG) products for two different regions of the globe (Korea and the U.S.) are provided, and a brief analysis related to IMERG performance in the extreme rainfall event associated with Hurricane Florence is discussed

A Study of Types of Sensors used in Remote Sensing

Of late, the science of Remote Sensing has been gaining a lot of interest and attention due to its wide variety of applications. Remotely sensed data can be used in various fields such as medicine, agriculture, engineering, weather forecasting, military tactics, disaster management etc. only to name a few. This article presents a study of the two categories of sensors namely optical and microwave which are used for remotely sensing the occurrence of disasters such as earthquakes, floods, landslides, avalanches, tropical cyclones and suspicious movements. The remotely sensed data acquired either through satellites or through ground based- synthetic aperture radar systems could be used to avert or mitigate a disaster or to perform a post-disaster analysis

Machine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars

2019 Spring.Includes bibliographical references.Precipitation measurement by satellite radar plays a significant role in researching the water circle and forecasting extreme weather event. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) has capability of providing a high-resolution vertical profile of precipitation over the tropics regions. Its successor, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR), can provide detailed information on the microphysical properties of precipitation particles, quantify particle size distribution and quantitatively measure light rain and falling snow. This thesis presents a novel Machine Learning system for ground-based and space borne radar rainfall estimation. The system first trains ground radar data for rainfall estimation using rainfall measurements from gauges and subsequently uses the ground radar based rainfall estimates to train spaceborne radar data in order to get space based rainfall product. Therein, data alignment between spaceborne and ground radar is conducted using the methodology proposed by Bolen and Chandrasekar (2013), which can minimize the effects of potential geometric distortion of spaceborne radar observations. For demonstration purposes, rainfall measurements from three rain gauge networks near Melbourne, Florida, are used for training and validation purposes. These three gauge networks, which are located in Kennedy Space Center (KSC), South Florida Water Management District (SFL), and St. Johns Water Management District (STJ), include 33, 46, and 99 rain gauge stations, respectively. Collocated ground radar observations from the National Weather Service (NWS) Weather Surveillance Radar – 1988 Doppler (WSR-88D) in Melbourne (i.e., KMLB radar) are trained with the gauge measurements. The trained model is then used to derive KMLB radar based rainfall product, which is used to train both TRMM PR and GPM DPR data collected from coincident overpasses events. The machine learning based rainfall product is compared against the standard satellite products, which shows great potential of the machine learning concept in satellite radar rainfall estimation. Also, the local rain maps generated by machine learning system at KMLB area are demonstrate the application potential

Feasibility of passive bistatic geosynchronous radar using comsat transmissions

Communication satellites in geosynchronous orbit are increasingly broadcasting digital signals with high bandwidth and high power. These signals are in principle well-suited to radar imaging and the study presented here is an initial feasibility study for a passive bistatic synthetic aperture radar using satellites in geosynchronous orbit (GEO). The persistent viewing possible from GEO could enable important new applications. The mission concept is outlined and studies of the available signal formats identify digital TV broadcasts in Ku-band as most suitable for radar imaging. The additional space hardware required is a dedicated receive channel, which could be implemented as a hosted payload at modest cost. Our findings so far suggest that the mission concept is feasible for coarse spatial resolution images and that it could therefore provide a low-cost technology demonstration of geosynchronous radar

Frequency diversity wideband digital receiver and signal processor for solid-state dual-polarimetric weather radars

2012 Summer.Includes bibliographical references.The recent spate in the use of solid-state transmitters for weather radar systems has unexceptionably revolutionized the research in meteorology. The solid-state transmitters allow transmission of low peak powers without losing the radar range resolution by allowing the use of pulse compression waveforms. In this research, a novel frequency-diversity wideband waveform is proposed and realized to extenuate the low sensitivity of solid-state radars and mitigate the blind range problem tied with the longer pulse compression waveforms. The latest developments in the computing landscape have permitted the design of wideband digital receivers which can process this novel waveform on Field Programmable Gate Array (FPGA) chips. In terms of signal processing, wideband systems are generally characterized by the fact that the bandwidth of the signal of interest is comparable to the sampled bandwidth; that is, a band of frequencies must be selected and filtered out from a comparable spectral window in which the signal might occur. The development of such a wideband digital receiver opens a window for exciting research opportunities for improved estimation of precipitation measurements for higher frequency systems such as X, Ku and Ka bands, satellite-borne radars and other solid-state ground-based radars. This research describes various unique challenges associated with the design of a multi-channel wideband receiver. The receiver consists of twelve channels which simultaneously downconvert and filter the digitized intermediate-frequency (IF) signal for radar data processing. The product processing for the multi-channel digital receiver mandates a software and network architecture which provides for generating and archiving a single meteorological product profile culled from multi-pulse profiles at an increased data date. The multi-channel digital receiver also continuously samples the transmit pulse for calibration of radar receiver gain and transmit power. The multi-channel digital receiver has been successfully deployed as a key component in the recently developed National Aeronautical and Space Administration (NASA) Global Precipitation Measurement (GPM) Dual-Frequency Dual-Polarization Doppler Radar (D3R). The D3R is the principal ground validation instrument for the precipitation measurements of the Dual Precipitation Radar (DPR) onboard the GPM Core Observatory satellite scheduled for launch in 2014. The D3R system employs two broadly separated frequencies at Ku- and Ka-bands that together make measurements for precipitation types which need higher sensitivity such as light rain, drizzle and snow. This research describes unique design space to configure the digital receiver for D3R at several processing levels. At length, this research presents analysis and results obtained by employing the multi-carrier waveforms for D3R during the 2012 GPM Cold-Season Precipitation Experiment (GCPEx) campaign in Canada

Synthetic Aperture Radar (SAR) data processing

The available and optimal methods for generating SAR imagery for NASA applications were identified. The SAR image quality and data processing requirements associated with these applications were studied. Mathematical operations and algorithms required to process sensor data into SAR imagery were defined. The architecture of SAR image formation processors was discussed, and technology necessary to implement the SAR data processors used in both general purpose and dedicated imaging systems was addressed