Method for inverse synthetic aperture radar imaging of space debris using improved genetic algorithm

Yu, Ning; Bai, Xueru; Zhou, Feng; Liu, Lei

doi:10.1049/iet-rsn.2016.0048

Cited by 13 publications

(3 citation statements)

References 20 publications

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“…Thus, the determination of the optimal discretization scheme is a constrained optimization problem, which has been proven to be NP-complete [51]. It is difficult to solve this kind of problem by traditional methods, and global optimization algorithms are more effective than traditional methods because their group search strategies and calculation methods do not depend on gradient information [52]. Global optimization algorithms for discretization mainly include the genetic algorithm (GA) [53], PSO [26], and ant colony optimization (ACO) [54].…”

Section: B Feasibility Analysis and Genetic Codingmentioning

confidence: 99%

A Feature Discretization Method Based on Fuzzy Rough Sets for High-Resolution Remote Sensing Big Data Under Linear Spectral Model

Chen

Huang

Wang

et al. 2022

IEEE Trans. Fuzzy Syst.

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As one of the most relevant data preprocessing techniques, discretization has played an important role in data mining, which is widely applied in industrial control. It can transform continuous features to discrete ones, thus improving the efficiency of data processing and adapting to learning algorithms that require discrete data as inputs. However, traditional discretization methods have shortcomings, such as highly complex programs, excessive numbers of intervals obtained, and significant loss of necessary information in the preprocessing of high-resolution remote sensing big data. Moreover, the large number of mixed pixels in the image is a primary reason for the uncertainty of remote sensing information systems, and current discretization methods are based on the assumption that one pixel only corresponds to the spectral information of a single object, without considering the influence of the uncertainty caused by a mixed spectrum, which causes the classification accuracy to drop after discretization. We propose a discretization method for high-resolution remote sensing big data. We determine the membership degree of each pixel in training samples through linear decomposition, and establish the individual fitness function based on a fuzzy rough model. An adaptive genetic algorithm selects discrete breakpoints, and a MapReduce framework calculates the individual fitness of the population in parallel, to obtain the optimal discretization scheme in the minimum time. Our method is compared to the best state-of-the-art discretization algorithms on the authentic remote sensing datasets. Experiments verified the effectiveness of the proposed method, which provides strong support for the subsequent processing of images.

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Section: B Feasibility Analysis and Genetic Codingmentioning

confidence: 99%

A Feature Discretization Method Based on Fuzzy Rough Sets for High-Resolution Remote Sensing Big Data Under Linear Spectral Model

Chen

Huang

Wang

et al. 2022

IEEE Trans. Fuzzy Syst.

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“…Among them, EAs are particularly popular. Recently, EAs have been successfully utilised in the fields of inverse synthetic aperture radar (SAR) imaging of space debris [16], multi‐band SAR MTD and imaging [17] and electronic support receiver search strategies optimisation [18]. Non‐dominated sorting genetic algorithm II (NSGA‐II) [19] is a mature and well‐known MOEA, which was used in many studies on optimisation including some in the field of radar engineering, e.g.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary multi‐objective optimisation of the pulse burst waveform in solid‐state VHF moving target detection radar

Jevtić¹,

Zogovic

Graovac³

2019

IET Radar, Sonar & Navigation

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“…Inverse synthetic aperture radar (ISAR) has the ability of achieving high-resolution radar image of moving targets in all weather and day/night conditions [1,2,3]. ISAR imaging is gradually gaining popularity in microwave remote sensing and national defense applications [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Analysis of TIADC channel mismatch effects on high-resolution range profile

Xiao

Zhang

2020

IEICE Electron. Express

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The wideband echo signals of the imaging radar can be processed by pulse compression to obtain high-resolution range profile (HRRP). The resolution of HRRP is proportional to the signal bandwidth. The pursuit of high resolution makes the bandwidth of radar transmitting signal larger and larger. And the corresponding sampling rate of receiver becomes so high that analog-to-digital conversion cannot be realized by a single chip correctly. Time-interleaved analog-to-digital converter (TIADC) technique increases sampling rate of radar receiver, but channel mismatches are brought in. Channel mismatches significantly deteriorate the dynamic performance of radar receiver. Effects of channel mismatch on SNR, SNDR and SFDR are studied in previous literatures. Derivation and simulation of TIADC channel mismatch effecting on HRRP are performed in this letter. Simulation results show that without considering the amplitude-frequency distortion and phase-frequency distortions of the radar system, when the gain mismatch is less than 0.7 times and the clock mismatch is less than 0.25 rad, the influence of the channel mismatch on the HRRP can be neglected. The results provide a reference for system design and compensation method, which are useful for the designer to determine whether complex channel mismatch calibration is necessary under specific application background.

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Method for inverse synthetic aperture radar imaging of space debris using improved genetic algorithm

Cited by 13 publications

References 20 publications

A Feature Discretization Method Based on Fuzzy Rough Sets for High-Resolution Remote Sensing Big Data Under Linear Spectral Model

A Feature Discretization Method Based on Fuzzy Rough Sets for High-Resolution Remote Sensing Big Data Under Linear Spectral Model

Evolutionary multi‐objective optimisation of the pulse burst waveform in solid‐state VHF moving target detection radar

Analysis of TIADC channel mismatch effects on high-resolution range profile

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