A Tip–Tilt and Piston Detection Approach for Segmented Telescopes

Shun Qin; Wai Kin Chan

doi:10.3390/photonics8010003

A Tip–Tilt and Piston Detection Approach for Segmented Telescopes

Photonics ◽

10.3390/photonics8010003 ◽

2020 ◽

Vol 8 (1) ◽

pp. 3

Author(s):

Shun Qin ◽

Wai Kin Chan

Keyword(s):

Error Detection ◽

Phase Retrieval ◽

Dynamic Range ◽

Detection Accuracy ◽

Wavefront Sensing ◽

Next Generation ◽

Large Aperture ◽

Detection Approach ◽

Segmented Mirror ◽

And Control

Accurate segmented mirror wavefront sensing and control is essential for next-generation large aperture telescope system design. In this paper, a direct tip–tilt and piston error detection technique based on model-based phase retrieval with multiple defocused images is proposed for segmented mirror wavefront sensing. In our technique, the tip–tilt and piston error are represented by a basis consisting of three basic plane functions with respect to the x, y, and z axis so that they can be parameterized by the coefficients of these bases; the coefficients then are solved by a non-linear optimization method with the defocus multi-images. Simulation results show that the proposed technique is capable of measuring high dynamic range wavefront error reaching 7λ, while resulting in high detection accuracy. The algorithm is demonstrated as robust to noise by introducing phase parameterization. In comparison, the proposed tip–tilt and piston error detection approach is much easier to implement than many existing methods, which usually introduce extra sensors and devices, as it is a technique based on multiple images. These characteristics make it promising for the application of wavefront sensing and control in next-generation large aperture telescopes.

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Developmental cryogenic active telescope testbed: a wavefront sensing and control testbed for the Next-Generation Space Telescope

10.1117/12.324517 ◽

1998 ◽

Cited By ~ 1

Author(s):

Claudia M. LeBoeuf ◽

Pamela S. Davila ◽

David C. Redding ◽

Armando Morrell ◽

Andrew E. Lowman ◽

...

Keyword(s):

Wavefront Sensing ◽

Next Generation ◽

Space Telescope ◽

And Control

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Initial test results from the Next Generation Space Telescope (NGST) wavefront sensing and control testbed (WCT)

10.1117/12.393972 ◽

2000 ◽

Cited By ~ 1

Author(s):

Charles W. Bowers ◽

Pamela S. Davila ◽

Bruce H. Dean ◽

Brendon D. Perkins ◽

Mark E. Wilson ◽

...

Keyword(s):

Wavefront Sensing ◽

Test Results ◽

Next Generation ◽

Initial Test ◽

Space Telescope ◽

And Control

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Wavefront sensing and control for a Next-Generation Space Telescope

10.1117/12.324494 ◽

1998 ◽

Cited By ~ 15

Author(s):

David C. Redding ◽

Scott A. Basinger ◽

Andrew E. Lowman ◽

Andrew Kissil ◽

Pierre Y. Bely ◽

...

Keyword(s):

Wavefront Sensing ◽

Next Generation ◽

Space Telescope ◽

And Control

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Complex wavefront sensing based on coherent diffraction imaging using vortex modulation

Scientific Reports ◽

10.1038/s41598-021-88523-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rujia Li ◽

Liangcai Cao

Keyword(s):

Phase Retrieval ◽

Dynamic Range ◽

A Priori ◽

Measured Intensity ◽

Physical Constraints ◽

Coherent Diffraction Imaging ◽

Effective Dynamic ◽

Ill Posed ◽

Retrieval Problem ◽

Diffraction Imaging

AbstractPhase retrieval seeks to reconstruct the phase from the measured intensity, which is an ill-posed problem. A phase retrieval problem can be solved with physical constraints by modulating the investigated complex wavefront. Orbital angular momentum has been recently employed as a type of reliable modulation. The topological charge l is robust during propagation when there is atmospheric turbulence. In this work, topological modulation is used to solve the phase retrieval problem. Topological modulation offers an effective dynamic range of intensity constraints for reconstruction. The maximum intensity value of the spectrum is reduced by a factor of 173 under topological modulation when l is 50. The phase is iteratively reconstructed without a priori knowledge. The stagnation problem during the iteration can be avoided using multiple topological modulations.

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Automatic Roadway Features Detection with Oriented Object Detection

Applied Sciences ◽

10.3390/app11083531 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3531

Author(s):

Hesham M. Eraqi ◽

Karim Soliman ◽

Dalia Said ◽

Omar R. Elezaby ◽

Mohamed N. Moustafa ◽

...

Keyword(s):

Object Detection ◽

Safety Evaluation ◽

Autonomous Driving ◽

Detection Accuracy ◽

The Road ◽

Detection Model ◽

Detection Approach ◽

Roadway Safety ◽

Safety Features ◽

Oriented Object

Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.

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Random Collective Representation-Based Detector with Multiple Features for Hyperspectral Images

Remote Sensing ◽

10.3390/rs13040721 ◽

2021 ◽

Vol 13 (4) ◽

pp. 721

Author(s):

Zhongheng Li ◽

Fang He ◽

Haojie Hu ◽

Fei Wang ◽

Weizhong Yu

Keyword(s):

Anomaly Detection ◽

Hyperspectral Image ◽

Spectral Feature ◽

Collaborative Representation ◽

Detection Accuracy ◽

Spatial Features ◽

Collective Representation ◽

Detection Approach ◽

Adaptive Weight ◽

Gabor Feature

Collaborative representation-based detector (CRD), as the most representative anomaly detection method, has been widely applied in the field of hyperspectral anomaly detection (HAD). However, the sliding dual window of the original CRD introduces high computational complexity. Moreover, most HAD models only consider a single spectral or spatial feature of the hyperspectral image (HSI), which is unhelpful for improving detection accuracy. To solve these problems, in terms of speed and accuracy, we propose a novel anomaly detection approach, named Random Collective Representation-based Detector with Multiple Feature (RCRDMF). This method includes the following steps. This method first extract the different features include spectral feature, Gabor feature, extended multiattribute profile (EMAP) feature, and extended morphological profile (EMP) feature matrix from the HSI image, which enables us to improve the accuracy of HAD by combining the multiple spectral and spatial features. The ensemble and random collaborative representation detector (ERCRD) method is then applied, which can improve the anomaly detection speed. Finally, an adaptive weight approach is proposed to calculate the weight for each feature. Experimental results on six hyperspectral datasets demonstrate that the proposed approach has the superiority over accuracy and speed.

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