Reconstruction of the Normalized Radar Cross Section Field From GNSS-R Delay-Doppler Map

Schiavulli, Domenico; Nunziata, Ferdinando; Pugliano, Giovanni; Migliaccio, M.

doi:10.1109/jstars.2014.2301019

Cited by 44 publications

(42 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the observed scene for one reflection can be divided into several areas with smaller size [22]. So, the spatial resolutions also depend on the algorithm of GNSS-R data processing.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Evaluation of GNSS-R Based on Future Fully Operational Global Multi-GNSS and Eight-LEO Constellations

Gao

Wang

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Spaceborne GNSS-R (global navigation satellite system reflectometry) is an innovative and powerful bistatic radar remote sensing technique that uses specialized GNSS-R instruments on LEO (low Earth orbit) satellites to receive GNSS L-band signals reflected by the Earth's surface. Unlike monostatic radar, the illuminated areas are elliptical regions centered on specular reflection points. Evaluation of the spatiotemporal resolution of the reflections is necessary at the GNSS-R mission design stage for various applications. However, not all specular reflection signals can be received because the size and location of the GNSS-R antenna's available reflecting ground coverage depends on parameters including the on-board receiver antenna gain, the signal frequency and power, the antenna face direction, and the LEO's altitude. Additionally, the number of available reflections is strongly related to the number of GNSS-R LEO and GNSS satellites. By 2020, the Galileo and BeiDou Navigation Satellite System (BDS) constellations are scheduled to be fully operational at global scale and nearly 120 multi-GNSS satellites, including Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) satellites, will be available for use as illuminators. In this paper, to evaluate the future capacity for repetitive GNSS-R observations, we propose a GNSS satellite selection method and simulate the orbit of eight-satellite LEO and partial multi-GNSS constellations. We then analyze the spatiotemporal distribution characteristics of the reflections in two cases: (1) When only GPS satellites are available; (2) when multi-GNSS satellites are available separately. Simulation and analysis results show that the multi-GNSS-R system has major advantages in terms of available satellite numbers and revisit times over the GPS-R system. Additionally, the spatial density of the specular reflections on the Earth's surface is related to the LEO inclination and constellation construction.

show abstract

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Evaluation of GNSS-R Based on Future Fully Operational Global Multi-GNSS and Eight-LEO Constellations

Gao

Wang

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Radar imaging has been widely used in many civilian and military fields due to its all-weather and day/night ability [1,2]. High resolution of side-looking and forward-squint imaging of motion platforms have been deeply discussed for many years [3,4].…”

Section: Introductionmentioning

confidence: 99%

Superresolution Imaging for Forward-Looking Scanning Radar With Generalized Gaussian Constraint

Zhang¹,

Huang²,

Zha³

et al. 2016

PIER M

View full text Add to dashboard Cite

Abstract-A maximum a posteriori (MAP) approach, based on the Bayesian criterion, is proposed to overcome the low cross-range resolution problem in forward-looking imaging. We adapt scanning radar system to record received data and exploit deconvolution method to enhance the real-aperture resolution because the received echo is the convolution of target scattering coefficient and antenna pattern. The Generalized Gaussian distribution is considered as the prior information of target scattering coefficient in MAP approach for the reason that it could express different target scattering coefficient properties with the control of statistic parameter. This constraint term makes the proposed algorithm useful in different applications. On the other hand, the reconstruction problem can also be viewed as the l p -norm (0 < p ≤ 2) regularization. Simulation results show the robustness of the proposed algorithm against additive noise compared with other superresolution methods.

show abstract

“…Following the same rational, in [10] the CLS is used to detect oil spill and cyclones included in a simulated marine scenario. In [11] an inversion scheme based on the 2-D Truncated Singular Value Decomposition (TSVD) approach is successfully tested against noisy DDMs including non homogeneous area, called targets. Furthermore, a rigorous discussion on the obtained resolution is performed and an area, named remote sensing zone (RSZ), is identified.…”

Section: Introductionmentioning

confidence: 99%

A computer-time effective scheme to reconstruct the radar image from a noisy GNSS-R DDM

Schiavulli

Lenti

Nunziata

et al. 2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

View full text Add to dashboard Cite

Global Navigation Satellite Systems - Reflectometry (GNSS-R) is an innovative and promising tool for remote sensing purposes. Several applications have been developed to extract geophysical information of the reflected scene from the measured delay waveforms and Delay-Doppler Maps (DDMs). Recently, techniques have been presented to deconvolve the DDMs to reconstruct the Normalize Radar Cross Section (NRCS) field of the observed area in order to provide added value products for imaging remote sensing. In this paper an effective reconstruction scheme, based on the Conjugate Gradient approach operating in Banach space, is proposed and successfully tested on simulated marine scenario

show abstract

Reconstruction of the Normalized Radar Cross Section Field From GNSS-R Delay-Doppler Map

Cited by 44 publications

References 17 publications

Spatiotemporal Evaluation of GNSS-R Based on Future Fully Operational Global Multi-GNSS and Eight-LEO Constellations

Spatiotemporal Evaluation of GNSS-R Based on Future Fully Operational Global Multi-GNSS and Eight-LEO Constellations

Superresolution Imaging for Forward-Looking Scanning Radar With Generalized Gaussian Constraint

A computer-time effective scheme to reconstruct the radar image from a noisy GNSS-R DDM

Contact Info

Product

Resources

About