GNSS-based BiSAR imaging using modified range migration algorithm

Zeng, Tao; Liu, FeiFeng; Antoniou, Michail; Cherniakov, M.

doi:10.1007/s11432-015-5366-y

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…This article is dedicated to the use of the GNSS signals for ship target imaging by resorting to the SAR technique because the ship translational motion is equivalent to the stationary Rx moving along the opposite direction with the same speed, similar to in strip-map SAR. Conventional GNSS-SAR algorithms [30], [31], [32], [33] have the knowledge of the radar platform velocity and then implement azimuth compression to fulfill imaging processing. However, in our case, the moving ship is a noncooperative target whose velocity is unknown.…”

Section: Introductionmentioning

confidence: 99%

Maritime Ship Target Imaging With GNSS-Based Passive Multistatic Radar

Chen

Yang

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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In the field of maritime surveillance, the global navigation satellite system (GNSS)-based passive radar has proven its potential for moving target detection (MTD), localization, and velocity estimation. The next stage is to investigate the possibility of obtaining the radar image of the moving ship for target recognition. However, the limited signal power budget of GNSS prevents the conventional inverse synthetic aperture radar technique that is based on target rotational motion and short observation time for GNSS-based passive radar imaging moving target. In this article, a two-stage imaging processing method relying on the target translational motion over a long observation time is proposed. The first stage confirms the presence of the target by a long-time MTD processing technique. In the second stage, based on the analysis of the Doppler history of the target signal in the slow-time domain, short-time Fourier transform and modified random sample consensus are combined to robustly estimate target velocity with reduced computation complexity. To obtain the focused bistatic image, azimuth compression is conducted by using the estimated target velocity. Finally, an image fusion operation is implemented to combine the bistatic images achievable from multiple satellites so that a multistatic image with high quality can be created. The effectiveness of the proposed method is confirmed by the real experimental results of three cargo ships illuminated by several satellites.

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Section: Introductionmentioning

confidence: 99%

Maritime Ship Target Imaging With GNSS-Based Passive Multistatic Radar

Chen

Yang

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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“…The transmitter is a GNSS satellite, while the receiver can be deployed on the ground, a moving vehicle, or an aircraft, resulting in various bistatic acquisition geometries. Several contributions to image formation algorithms for special bistatic SAR topologies can be found in recent papers, such as the range-Doppler algorithm [7,8], back-projection algorithm [9], range migration algorithm [10], and some improved algorithms [11][12][13]. The validation of the GNSS-SAR image formation algorithms has paved the way for GNSS-SAR application.…”

Section: Introductionmentioning

confidence: 99%

Moving Target Imaging Using GNSS-Based Passive Bistatic Synthetic Aperture Radar

Yang

Chen

et al. 2020

Remote Sensing

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Current studies of global navigation satellite systems (GNSS)-based bistatic synthetic aperture radar (GNSS-SAR) is focused on static objects on land. However, moving target imaging is also very significant for modern SAR systems. Imaging a moving target has two main problems. One is the unknown range cell migration; the other is the motion parameter estimation, such as the target’s velocity. This paper proposes a moving target imaging formation algorithm for GNSS-SAR. First, an approximate bistatic range history is derived to describe the phase variation of the target signal along the azimuth time. Then, a keystone transform is employed to correct the range cell migration. To address the motion parameter estimation, a chirp rate estimation method based on short-time Fourier transform and random sample consensus is proposed with high processing efficiency and robust estimation errors in low signal-to-noise ratio scenes. The estimated chirp rate can calculate the target’s velocity. Finally, azimuth compression derivation is performed to accomplish GNSS-SAR imaging. A maritime experimental campaign is conducted to validate the effectiveness of the proposed algorithm. The two cargo ships in the SAR images have good accordance with the ground truth in terms of the target-to-receiver vertical distances along the range and the ships’ length along the cross-range.

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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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GNSS-based BiSAR imaging using modified range migration algorithm

Cited by 6 publications

References 16 publications

Maritime Ship Target Imaging With GNSS-Based Passive Multistatic Radar

Maritime Ship Target Imaging With GNSS-Based Passive Multistatic Radar

Moving Target Imaging Using GNSS-Based Passive Bistatic Synthetic Aperture Radar

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

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