A Novel General Imaging Formation Algorithm for GNSS-Based Bistatic SAR

Zeng, Hongbin; Wang, Pengbo; Chen, Jie; Liu, Wei; Liu, Ge; Yang, Wei

doi:10.3390/s16030294

Cited by 25 publications

(21 citation statements)

References 33 publications

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“…In a GNSS system, the transmitted signal is a code-modulated continuous wave, the echo signal of GNSS-based passive radar forms a one-dimensional vector, and the target reflected GNSS signals from all visible satellites are received by the receiver antenna. According to [16], after quadrature demodulation, the target echo signal can be modeled as:…”

Section: Air Target Echo Signal Model Of Gnss-based Passive Radarmentioning

confidence: 99%

“…Then, the accompanying problem is long duration, 2-dimensional (2-D), coherent integration processing. For static targets in GNSS-based passive radar, 2-D coherent gain can be easily obtained by synthetic aperture radar (SAR) imaging algorithms [14][15][16]. However, for air moving targets, like an aircraft, it is really difficult to perform coherent processing in either the azimuth or the range dimension with unknown target motion parameters.…”

Section: Introductionmentioning

confidence: 99%

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2-D Coherent Integration Processing and Detecting of Aircrafts Using GNSS-Based Passive Radar

et al. 2018

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Long time coherent integration is a vital method for improving the detection ability of global navigation satellite system (GNSS)-based passive radar, because the GNSS signal is not radar-designed and its power level is very low. For aircraft detection, the large range cell migration (RCM) and Doppler frequency migration (DFM) will seriously affect the coherent processing of azimuth signals, and the traditional range match filter will also be mismatched due to the Doppler-intolerant characteristic of GNSS signals. Accordingly, the energy loss of 2-dimensional (2-D) coherent processing is inevitable in traditional methods. In this paper, a novel 2-D coherent integration processing and algorithm for aircraft target detection is proposed. For azimuth processing, a modified Radon Fourier Transform (RFT) with range-walk removal and Doppler rate estimation is performed. In respect to range compression, a modified matched filter with a shifting Doppler is applied. As a result, the signal will be accurately focused in the range-Doppler domain, and a sufficiently high SNR can be obtained for aircraft detection with a moving target detector. Numerical simulations demonstrate that the range-Doppler parameters of an aircraft target can be obtained, and the position and velocity of the aircraft can be estimated accurately by multiple observation geometries due to abundant GNSS resources. The experimental results also illustrate that the blind Doppler sidelobe is suppressed effectively and the proposed algorithm has a good performance even in the presence of Doppler ambiguity.

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Section: Air Target Echo Signal Model Of Gnss-based Passive Radarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

2-D Coherent Integration Processing and Detecting of Aircrafts Using GNSS-Based Passive Radar

et al. 2018

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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%

“…Although having the ability of handling different configurations, these algorithms are very time consuming. In order to handle the spatial variation of the echo data, some two step algorithms [24,25] were proposed for GNSS-R BSAR. The general idea of those algorithms is very similar: a bulk compensation for coarse focusing and then a pointwise compensation for the residual RCM and phase error.…”

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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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“…In addition, they discussed spatial decorrelation in GNSS-based SAR coherent change detection [12] and performed coherent change detection experiments [13]. Other research teams including Beihang University, Collaborative Innovation Center of Geospatial Technology, University of Sheffield and University of New South Wales proposed a new imaging algorithm for GNSS-based bistatic SAR, which relies on the point target spectrum model of the echo and makes the echo data focused more accurately and efficiently [14]. The research team from the Beijing Institute of Technology conducted imaging experiments based on Beidou Medium Earth Orbit (MEO) satellites [15,16] to detect corner reflector and transponder with strong scattering coefficients.…”

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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A Novel General Imaging Formation Algorithm for GNSS-Based Bistatic SAR

Cited by 25 publications

References 33 publications

2-D Coherent Integration Processing and Detecting of Aircrafts Using GNSS-Based Passive Radar

2-D Coherent Integration Processing and Detecting of Aircrafts Using GNSS-Based Passive 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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