SAR-based augmented integrity navigation architecture

Greco, Mario; Querry, S.; Pinelli, G.; Kulpa, Krzysztof; Samczyński, Piotr; Gromek, Damian; Gromek, Artur; Malanowski, Mateusz; Querry, B.; Bonsignore, A.

doi:10.1109/irs.2012.6233320

Cited by 15 publications

(7 citation statements)

References 4 publications

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“…Figure 2 shows the overview of the incremental SfM workflow, which contains the following steps: (1) feature extraction; (2) feature matching; (3) geometric verification; (4) reconstruction initialization; (5) image registration; (6) triangulation; and (7) bundle adjustment. To generalize, the SfM approach might be divided into two main parts: the correspondence search phase (1-3) and iterative reconstruction phase (4)(5)(6). Based on these two phases, the estimation of the camera position for each image as well as a 3D reconstructed tie point, called a spare point cloud [20], can be done.…”

Section: The Principles Of the Structure From Motionmentioning

confidence: 99%

“…A recently developed technique is the use of Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) radar for navigation correction [4][5][6][7]. Radio waves used in this technique can be easily used during cloud cover, night, and rain, which makes this method universal and independent of the weather conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of this paper were also involved in this work, and as a result have developed a concept of the system and proved that SAR/InSAR sensors can be successfully used to support navigational devices. The results of this work were published by the authors in [4][5][6]. Initially, the concept was proved only using simulations at the technical readiness level (TRL) 3-4 in the nine-degree scale, where 9 denotes the system prototype after all the required certifications.…”

Section: Introductionmentioning

confidence: 99%

“…By finding characteristic points in both images and applying themodified version of the Structure from Motion (SfM) technique, an error can be estimated, which in turn provides the navigation drift correction in the flying platform. In the authors' opinion, the concept of using the ASIFT technique is interest regarding the checking of the efficiency and precision of mismatched SAR and optical image calculations, which might be further used to correct platform navigation devices according to the algorithms developed by the authors in their previous work [4][5][6]. To the authors' knowledge, an innovative approach to the shift and rotation estimation between SAR and optical images by also applying SfM techniques has not been used for navigation drift correction on a flying platform, on which the authors of this paper are currently working.…”

Section: Introductionmentioning

confidence: 99%

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Geometrical Matching of SAR and Optical Images Utilizing ASIFT Features for SAR-based Navigation Aided Systems

Markiewicz

Abratkiewicz

Gromek

et al. 2019

Sensors

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This article presents a new approach to the estimation of shift and rotation between two images from different kinds of imaging sensors. The first of the image is an orthophotomap that is created using optical sensors with georeference information. The second one is created utilizing a Synthetic Aperture Radar (SAR) sensor.The proposed solution can be mounted on a flying platform, and, during the flight, the obtained SAR images are compared with the reference optical images, and thus it is possible to calculate the shift and rotation between these two images and then the direct georeferencing error. Since both images have georeference information, it is possible to calculate the navigation correction in cases when the drift of the calculated trajectory is expected. The method can be used in platforms where there is no satellite navigation signal and the trajectory is calculated on the basis of an inertial navigation system, which is characterized by a significant error. The proposed method of estimating the navigation error utilizing Affine Scale-Invariant Feature Transform (ASIFT) and Structure from Motion (SfM) is described, and techniques for improving the quality of SAR imaging using despeckling filters are presented. The methodology was tested and verified using real-life SAR images. Differences between the results obtained for a few selected despeckling methods were compared and commented on. Deep investigation of the nature of the SAR imaging technique and noise creation character allows new algorithms to be developed, which can be implemented on flying platforms to support existing navigation systems in which trajectory error occurs.

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Section: The Principles Of the Structure From Motionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Geometrical Matching of SAR and Optical Images Utilizing ASIFT Features for SAR-based Navigation Aided Systems

Markiewicz

Abratkiewicz

Gromek

et al. 2019

Sensors

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show abstract

“…This problem can affect in particular medium-altitude long-endurance (MALE) UAV, which has to fly for thousands of kilometers before reaching its target. Cumulative drift (up to hundreds of meters) during a long endurance flight can lead the UAV to miss the planned target with catastrophic consequences on the mission [ 4 ]. Thus, future guidance systems for UAV autonomous missions have challenging requirements for high reliability and integrity.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Using Synthetic Aperture Radar to Aid UAV Navigation

Nitti¹,

Bovenga

Chiaradia

et al. 2015

Sensors

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This study explores the potential of Synthetic Aperture Radar (SAR) to aid Unmanned Aerial Vehicle (UAV) navigation when Inertial Navigation System (INS) measurements are not accurate enough to eliminate drifts from a planned trajectory. This problem can affect medium-altitude long-endurance (MALE) UAV class, which permits heavy and wide payloads (as required by SAR) and flights for thousands of kilometres accumulating large drifts. The basic idea is to infer position and attitude of an aerial platform by inspecting both amplitude and phase of SAR images acquired onboard. For the amplitude-based approach, the system navigation corrections are obtained by matching the actual coordinates of ground landmarks with those automatically extracted from the SAR image. When the use of SAR amplitude is unfeasible, the phase content can be exploited through SAR interferometry by using a reference Digital Terrain Model (DTM). A feasibility analysis was carried out to derive system requirements by exploring both radiometric and geometric parameters of the acquisition setting. We showed that MALE UAV, specific commercial navigation sensors and SAR systems, typical landmark position accuracy and classes, and available DTMs lead to estimate UAV coordinates with errors bounded within ±12 m, thus making feasible the proposed SAR-based backup system.

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Gps-Denied Navigation Using Sar Images And Neural Networks

White

Wheeler

Lindstrom

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Unmanned aerial vehicles (UAV) often rely on GPS for navigation. GPS signals, however, are very low in power and easily jammed or otherwise disrupted. This paper presents a method for determining the navigation errors present at the beginning of a GPS-denied period utilizing data from a synthetic aperture radar (SAR) system. This is accomplished by comparing an online-generated SAR image with a reference image obtained a priori. The distortions relative to the reference image are learned and exploited with a convolutional neural network to recover the initial navigational errors, which can be used to recover the true flight trajectory throughout the synthetic aperture. The proposed neural network approach is able to learn to predict the initial errors on both simulated and real SAR image data.

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SAR-based augmented integrity navigation architecture

Cited by 15 publications

References 4 publications

Geometrical Matching of SAR and Optical Images Utilizing ASIFT Features for SAR-based Navigation Aided Systems

Geometrical Matching of SAR and Optical Images Utilizing ASIFT Features for SAR-based Navigation Aided Systems

Feasibility of Using Synthetic Aperture Radar to Aid UAV Navigation

Gps-Denied Navigation Using Sar Images And Neural Networks

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