An overview of GNSS remote sensing

Abstract

The Global Navigation Satellite System (GNSS) signals are always available, globally, and the signal structures are well known, except for those dedicated to military use. They also have some distinctive characteristics, including the use of L-band frequencies, which are particularly suited for remote sensing purposes. The idea of using GNSS signals for remote sensing -the atmosphere, oceans or Earth surface -was first proposed more than two decades ago. Since then, GNSS remote sensing has been intensively investigated in terms of proof of concept studies, signal processing methodologies, theory and algorithm development, and various satellite-borne, airborne and ground-based experiments. It has been demonstrated that GNSS remote sensing can be used as an alternative passive remote sensing technology. Space agencies such as NASA, NOAA, EUMETSAT and ESA have already funded, or will fund in the future, a number of projects/missions which focus on a variety of GNSS remote sensing applications. It is envisaged that GNSS remote sensing can be either exploited to perform remote sensing tasks on an independent basis or combined with other techniques to address more complex applications. This paper provides an overview of the state of the art of this relatively new and, in some respects, underutilised remote sensing technique. Also addressed are relevant challenging issues associated with GNSS remote sensing services and the performance enhancement of GNSS remote sensing to accurately and reliably retrieve a range of geophysical parameters. Review

Rateless coding transmission over multi-state dying erasure channel for SATCOM

“…The satellite must wait for recharging battery cells or restarting device, so the data transmission will break off unexpectedly until the next connection is established. We define the channel life span of MDEC as L, which follows an exponential distribution with mean 1 λ or a non-negative Gaussian distribution with mean μ and variance σ 2 …”

“…We choose three classical degree distributions similarly to the method in [19] and combine them to form a new degree distribution to improve the intermediate performance z with an assumption of finite code length k. That is, (1) (2) (x) = x 2 , and the weak robust soliton distribution [14]: (3) …”

“…This observation motivates us to linearly combine (1) , (2) , and (3) to form a weighted degree distribution LC (x) shown below:…”

GNSS remote sensing

“…The topics investigated include sea level measurement, snow depth estimation, snow water equivalent estimation, soil moisture estimation, buried object detection, and simplified GNSS signal processing techniques. The review paper [1] states that the global availability of GNSS signals provides the ideal means of observing different parameters of the Earth's atmosphere, land, and ocean surface. The paper summarizes developments in theory and signal processing methodologies and mentions a number of satellite-borne, airborne, and ground-based experiments relating to GNSS reflectometry and refractometry.…”

Improved Modeling of Global Ionospheric Total Electron Content Using Prior Information

“…Beyond that, ionosphere also affects L-band synthetic aperture radar (SAR) data [17][18][19], GNSS-reflectometry from space and satellite altimeter data [20][21][22]. Moreover, a growing number of microsatellites gaining broad applications need ionospheric correction with regards to calibration of remote sensing [23][24][25][26].…”