A new source extraction algorithm for optical space debris observation

Sun, Ruijing; Zhao, Chunjiang

doi:10.1088/1674-4527/13/5/011

Cited by 28 publications

(10 citation statements)

References 18 publications

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“…The real‐time tracking process of the space dim target is mainly to realize the correlation and prediction of the trace after the detection process. The on‐board calculation of the tracking needs to be completed within milliseconds, and it needs to reach the level of microsecond at high frame frequency 15 . For the real‐time tracking of space dim targets, it is necessary to complete the general tracking calculation acceleration in response to different space areas and complex backgrounds, which puts forward higher requirements for the engineering implementation architecture 16 .…”

Section: Introductionmentioning

confidence: 99%

Parallel accelerated computing architecture for dim target tracking on‐board

Yu,

Huang,

et al. 2023

Computational Intelligence

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The real‐time tracking process of dim targets in space is mainly achieved through the correlation and prediction of dots after the detection and calculation process. The on‐board calculation of the tracking needs to be completed in milliseconds, and it needs to reach the microsecond level at high frame rates. For real‐time tracking of dim targets in space, it is necessary to achieve universal tracking calculation acceleration in response to different space regions and complex backgrounds, which poses high requirements for engineering implementation architecture. This paper designs a Kalman filter calculation based on digital logic parallel acceleration architecture for real‐time solution of dim target tracking on‐board. A unified architecture of Vector Processing Element (VPE) was established for the calculation of Kalman filtering matrix, and an array computing structure based on VPE was designed to decompose the entire filtering process and form a parallel pipelined data stream. The prediction errors under different fixed point bit widths were analyzed and deduced, and the guidance methods for selecting the optimal bit width based on the statistical results were provided. The entire design was engineered based on Xilinx's XC7K325T, resulting in an energy efficiency improvement compared to previous designs. The single iteration calculation time does not exceed 0.7 microseconds, which can meet the current high frame rate target tracking requirements. The effectiveness of this design has been verified through simulation of random trajectory data, which is consistent with the theoretical calculation error.

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Section: Introductionmentioning

confidence: 99%

Parallel accelerated computing architecture for dim target tracking on‐board

Yu,

Huang,

et al. 2023

Computational Intelligence

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“…Nunez et al [15] proposed an image deconvolution method based on the Richardson-Lucy (R-L) algorithm, which is based on the maximum likelihood solution but did not reach the maximum. Sun et al [16], [17] implemented a pipeline to detect dim objects by using median filtering, mathematical morphology, and global thresholding. These methods also require prior information of the objects, and have difficulty to detect space debris without the prior information.…”

Section: Introductionmentioning

confidence: 99%

Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

Xiang

Ersoy

et al. 2020

IEEE Access

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Space debris detection is important in space situation awareness and space asset protection. In this paper, we propose a method to detect space debris using feature learning of candidate regions. The acquired optical image sequences are first processed to remove hot pixels and flicker noise, and the nonuniform background information is removed by the proposed one dimensional mean iteration method. Then, the feature learning of candidate regions (FLCR) method is proposed to extract the candidate regions and to detect space debris. The candidate regions of space debris are precisely extracted, and then classified by a trained deep learning network. The feature learning model is trained using a large number of simulated space debris with different signal to noise ratios (SNRs) and motion parameters, instead of using real space debris, which make it difficult to extract a sufficient number of real space debris with diverse parameters in optical image sequences. Finally, the candidate regions are precisely placed in the optical image sequences. The experiment is performed using the simulated data and acquired image sequences. The results show that the proposed method has good performance when estimating and removing background, and it can detect low SNR space debris with high detection probability.INDEX TERMS Space debris detection, background estimation, candidate region extraction, deep learning.

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“…Nevertheless, the way of moving telescope is not suitable for our large telescope, owing to the jitter error produced by back and forth movement of telescope. Sun has put forward a detection pipeline through median filtering and mathematical morphology [14], in which six frames are employed to extract objects, and the detection ability for faint objects is improved. Moreover, both detection accuracy and detection efficiency are important to the space debris detection system.…”

Section: Introductionmentioning

confidence: 99%

Effect Analysis of Optical Masking Algorithm for GEO Space Debris Detection

Kong

Zhou

2019

International Journal of Optics

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A method with high detection rate, low false-alarm rate, and low computational cost is presented for removing stars and noise and detecting space debris with signal-to-noise ratio (SNR>3) in consecutive raw frames. The top-hat transformation is implemented firstly to remove background, then a masking technique is proposed to remove stars, and finally, a weighted algorithm is used to detect the pieces of space debris. The simulation samples are images taken by 600 mm ground-based telescope. And a series of simulation targets are added to the image in order to test the detection rate and false-alarm rate of different SNRs. The telescope in this paper is worked in “staring target mode.” The experimental results show that the proposed method can detect space debris effectively with low false-alarm by only three frames. When the SNR is higher than 3, the detection probability can reach 94%, and the false-alarm rate is below 2%. The running time of this algorithm is within 1 second. Additionally, algorithm performance tests in different regions are also carried out. A large set of image sequences are tested, which proves the stableness and effectiveness of the proposed method.

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A new source extraction algorithm for optical space debris observation

Cited by 28 publications

References 18 publications

Parallel accelerated computing architecture for dim target tracking on‐board

Parallel accelerated computing architecture for dim target tracking on‐board

Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

Effect Analysis of Optical Masking Algorithm for GEO Space Debris Detection

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