Spatial Resolution Improvement in GNSS-Based SAR Using Multistatic Acquisitions and Feature Extraction

Santi, Fabrizio; Bucciarelli, Marta; Pastina, Debora; Antoniou, Michail; Cherniakov, M.

doi:10.1109/tgrs.2016.2583784

Cited by 63 publications

(49 citation statements)

References 34 publications

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“…The analysis of the GNSS reflected signals (GNSS-Reflectometry) has brought to a number of wellestablished technologies to remotely sense the atmosphere and ionosphere, ocean, land surface and cryosphere [9] [10]. In the field of radar sensors, the exploitation of GNSS signals has been investigated from several years for passive synthetic aperture radar imaging [11]- [18], with the ultimate goal to achieve Maritime moving target long time integration for GNSS-based passive bistatic radar persistent local area monitoring. With specific regard to surveillance purposes, some studies have been conducted for the detection of aerial targets [19]- [21], whereas only few investigations have been carried out for maritime surveillance purposes.…”

Section: Introductionmentioning

confidence: 99%

Maritime Moving Target Long Time Integration for GNSS-Based Passive Bistatic Radar

Pastina

Santi

Pieralice

et al. 2018

IEEE Trans. Aerosp. Electron. Syst.

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Section: Introductionmentioning

confidence: 99%

Maritime Moving Target Long Time Integration for GNSS-Based Passive Bistatic Radar

Pastina

Santi

Pieralice

et al. 2018

IEEE Trans. Aerosp. Electron. Syst.

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“…GNSS-R Bi-static Synthetic Aperture Radar (BSAR) is one of the new applications of the GNSS-R [15][16][17][18][19][20][21][22][23][24][25]. It uses the GNSS signal as a non-cooperative illuminator to obtain an image of the Earth's surface.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, commonly used imaging algorithms for GNSS-R BSAR are usually based on the Back-Projection Algorithm (BPA) [15][16][17][18][19][20][21][22][23]. Although having the ability of handling different configurations, these algorithms are very time consuming.…”

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confidence: 99%

An Efficient Imaging Algorithm for GNSS-R Bi-Static SAR

et al. 2019

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Global Navigation Satellite System Reflectometry (GNSS-R) based Bi-static Synthetic Aperture Radar (BSAR) is becoming more and more important in remote sensing, given its low power, low mass, low cost, and real-time global coverage capability. Due to its complex configuration, the imaging for GNSS-R BSAR is usually based on the Back-Projection Algorithm (BPA), which is very time consuming. In this paper, an efficient and general imaging algorithm for GNSS-R BSAR is presented. A Two Step Range Cell Migration (TSRCM) correction is firstly applied. The first step roughly compensates the RCM and Doppler phase caused by the motion of the transmitter, which simplifies the SAR data into the quasi-mono-static case. The second step removes the residual RCM caused by the motion of the receiver using the modified frequency scaling algorithm. Then, a cubic phase perturbation operation is introduced to equalize the Doppler frequency modulation rate along the same range cell. Finally, azimuth phase compensation and geometric correction are completed to obtain the focused SAR image. A simulation and experiment are conducted to demonstrate the feasibility of the proposed algorithm, showing that the proposed algorithm is more efficient than the BPA, without causing significant degradation in imaging quality.

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“…They also studied the feasibility of improving range resolution by joint Galileo E5 signals [8], multistatic acquisitions and feature extraction [9][10][11]. In addition, they discussed spatial decorrelation in GNSS-based SAR coherent change detection [12] and performed coherent change detection experiments [13].…”

Section: Introductionmentioning

confidence: 99%

GNSS Imaging: A Case Study of Tree Detection Based on Beidou Geo Satellites

Yan¹,

Zhou²,

Gong³

2018

PIER C

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Abstract-Curved trajectories of traditional navigation satellites limit their performance in bistatic radar imaging system. Instead of using common Inclined Geosynchronous Orbit (IGSO)/Medium Earth Orbit (MEO) satellites in motion, a new global navigation satellite system (GNSS) imaging system based on Beidou geo synchronous orbit (GEO) satellites is presented to deal with this problem. The most prominent feature of GEO satellites is that they are still relative to the earth. This work includes three parts. First, a reasonable parallel assumption is provided to simplify the geometric topology of the imaging system, and the relevant path delay formula is deduced. Second, the principle of imaging based on multiple GEO satellites is proposed, and the simulation result is presented. Third, the entire signal processing, which uses a multi-correlator, is designed to improve the range resolution. Two imaging experiments targeting at the trees are described and conducted in Wuhan University to verify the imaging system. The first experiment is targeted at isolated trees, and the second experiment is focused on groves along the road. Conclusion can be obtained: the imaging result is highly consistent with the imaging area, which validates the feasibility of the method and confirms the potential use of GNSS imaging system in forest monitoring.

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Spatial Resolution Improvement in GNSS-Based SAR Using Multistatic Acquisitions and Feature Extraction

Cited by 63 publications

References 34 publications

Maritime Moving Target Long Time Integration for GNSS-Based Passive Bistatic Radar

Maritime Moving Target Long Time Integration for GNSS-Based Passive Bistatic Radar

An Efficient Imaging Algorithm for GNSS-R Bi-Static SAR

GNSS Imaging: A Case Study of Tree Detection Based on Beidou Geo Satellites

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