Ultra-Quick pulsar positioning and velocimetry method based on GA-optimized quantum CS

Daliang Wu; Jin Wu; Jin Liu; Xin Ma; Zhiwei Kang

doi:10.1109/CCDC55256.2022.10034143

Ultra-Quick pulsar positioning and velocimetry method based on GA-optimized quantum CS

Daliang Wu^*
, Jin Wu
, Jin Liu^*
, Xin Ma
, Zhiwei Kang

^*Corresponding author for this work

School of Instrumentation and Optoelectronic Engineering

Wuhan University of Science and Technology
Hunan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The size of the measurement matrix in compressive sensing (CS) is proportional to calculation cost. To reduce the calculation cost, we utilize genetic algorithm (GA) to decrease the size of the measurement mother matrix, and propose an ultra-quick pulsar positioning and velocimetry method based on GA-optimized quantum CS (GQCS). There are two properties of the quantum measurement matrix. One is that the performance of the quantum measurement sub-matrices is different. The other one is that the combination of the quantum measurement sub-matrices affects the performance of the positioning and velocimetry method. Considering the randomness of the quantum measurement matrix, we select the quantum measurement sub-matrices from the quantum measurement mother matrix through GA. The quantum measurement matrix is divided into multiple sub-matrices. One gene in the chromosome determines whether the corresponding sub-matrix is retained or abandoned. The object of the fitness function is the estimated estimation errors of the quantum-based CS pulsar positioning and velocimetry (QCS). Through iteration, GA provides the sub-optimal combination of the quantum measurement sub-matrices, forming a small-sized and high-performance quantum measurement matrix. Simulation results indicate that the GQCS has a lower calculation cost and higher accuracy compared with the QCS.

Original language	English
Title of host publication	Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4530-4535
Number of pages	6
ISBN (Electronic)	9781665478960
DOIs	https://doi.org/10.1109/CCDC55256.2022.10034143
State	Published - 2022
Event	34th Chinese Control and Decision Conference, CCDC 2022 - Hefei, China Duration: 15 Aug 2022 → 17 Aug 2022

Publication series

Name	Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022

Conference

Conference	34th Chinese Control and Decision Conference, CCDC 2022
Country/Territory	China
City	Hefei
Period	15/08/22 → 17/08/22

Keywords

CS
GA
X-ray pulsar
positioning
velocimetry

Access to Document

10.1109/CCDC55256.2022.10034143

Cite this

Wu, D., Wu, J., Liu, J., Ma, X., & Kang, Z. (2022). Ultra-Quick pulsar positioning and velocimetry method based on GA-optimized quantum CS. In Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022 (pp. 4530-4535). (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC55256.2022.10034143

@inproceedings{b3b60fa504c849619bd8c76af20c3e51,

title = "Ultra-Quick pulsar positioning and velocimetry method based on GA-optimized quantum CS",

abstract = "The size of the measurement matrix in compressive sensing (CS) is proportional to calculation cost. To reduce the calculation cost, we utilize genetic algorithm (GA) to decrease the size of the measurement mother matrix, and propose an ultra-quick pulsar positioning and velocimetry method based on GA-optimized quantum CS (GQCS). There are two properties of the quantum measurement matrix. One is that the performance of the quantum measurement sub-matrices is different. The other one is that the combination of the quantum measurement sub-matrices affects the performance of the positioning and velocimetry method. Considering the randomness of the quantum measurement matrix, we select the quantum measurement sub-matrices from the quantum measurement mother matrix through GA. The quantum measurement matrix is divided into multiple sub-matrices. One gene in the chromosome determines whether the corresponding sub-matrix is retained or abandoned. The object of the fitness function is the estimated estimation errors of the quantum-based CS pulsar positioning and velocimetry (QCS). Through iteration, GA provides the sub-optimal combination of the quantum measurement sub-matrices, forming a small-sized and high-performance quantum measurement matrix. Simulation results indicate that the GQCS has a lower calculation cost and higher accuracy compared with the QCS.",

keywords = "CS, GA, X-ray pulsar, positioning, velocimetry",

author = "Daliang Wu and Jin Wu and Jin Liu and Xin Ma and Zhiwei Kang",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 34th Chinese Control and Decision Conference, CCDC 2022 ; Conference date: 15-08-2022 Through 17-08-2022",

year = "2022",

doi = "10.1109/CCDC55256.2022.10034143",

language = "英语",

series = "Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4530--4535",

booktitle = "Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022",

address = "美国",

}

Wu, D, Wu, J, Liu, J, Ma, X & Kang, Z 2022, Ultra-Quick pulsar positioning and velocimetry method based on GA-optimized quantum CS. in Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022, Institute of Electrical and Electronics Engineers Inc., pp. 4530-4535, 34th Chinese Control and Decision Conference, CCDC 2022, Hefei, China, 15/08/22. https://doi.org/10.1109/CCDC55256.2022.10034143

Ultra-Quick pulsar positioning and velocimetry method based on GA-optimized quantum CS. / Wu, Daliang; Wu, Jin; Liu, Jin et al.
Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 4530-4535 (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Ultra-Quick pulsar positioning and velocimetry method based on GA-optimized quantum CS

AU - Wu, Daliang

AU - Wu, Jin

AU - Liu, Jin

AU - Ma, Xin

AU - Kang, Zhiwei

PY - 2022

Y1 - 2022

N2 - The size of the measurement matrix in compressive sensing (CS) is proportional to calculation cost. To reduce the calculation cost, we utilize genetic algorithm (GA) to decrease the size of the measurement mother matrix, and propose an ultra-quick pulsar positioning and velocimetry method based on GA-optimized quantum CS (GQCS). There are two properties of the quantum measurement matrix. One is that the performance of the quantum measurement sub-matrices is different. The other one is that the combination of the quantum measurement sub-matrices affects the performance of the positioning and velocimetry method. Considering the randomness of the quantum measurement matrix, we select the quantum measurement sub-matrices from the quantum measurement mother matrix through GA. The quantum measurement matrix is divided into multiple sub-matrices. One gene in the chromosome determines whether the corresponding sub-matrix is retained or abandoned. The object of the fitness function is the estimated estimation errors of the quantum-based CS pulsar positioning and velocimetry (QCS). Through iteration, GA provides the sub-optimal combination of the quantum measurement sub-matrices, forming a small-sized and high-performance quantum measurement matrix. Simulation results indicate that the GQCS has a lower calculation cost and higher accuracy compared with the QCS.

AB - The size of the measurement matrix in compressive sensing (CS) is proportional to calculation cost. To reduce the calculation cost, we utilize genetic algorithm (GA) to decrease the size of the measurement mother matrix, and propose an ultra-quick pulsar positioning and velocimetry method based on GA-optimized quantum CS (GQCS). There are two properties of the quantum measurement matrix. One is that the performance of the quantum measurement sub-matrices is different. The other one is that the combination of the quantum measurement sub-matrices affects the performance of the positioning and velocimetry method. Considering the randomness of the quantum measurement matrix, we select the quantum measurement sub-matrices from the quantum measurement mother matrix through GA. The quantum measurement matrix is divided into multiple sub-matrices. One gene in the chromosome determines whether the corresponding sub-matrix is retained or abandoned. The object of the fitness function is the estimated estimation errors of the quantum-based CS pulsar positioning and velocimetry (QCS). Through iteration, GA provides the sub-optimal combination of the quantum measurement sub-matrices, forming a small-sized and high-performance quantum measurement matrix. Simulation results indicate that the GQCS has a lower calculation cost and higher accuracy compared with the QCS.

KW - CS

KW - GA

KW - X-ray pulsar

KW - positioning

KW - velocimetry

UR - https://www.scopus.com/pages/publications/85149566949

U2 - 10.1109/CCDC55256.2022.10034143

DO - 10.1109/CCDC55256.2022.10034143

M3 - 会议稿件

AN - SCOPUS:85149566949

T3 - Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022

SP - 4530

EP - 4535

BT - Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 34th Chinese Control and Decision Conference, CCDC 2022

Y2 - 15 August 2022 through 17 August 2022

ER -

Wu D, Wu J, Liu J, Ma X, Kang Z. Ultra-Quick pulsar positioning and velocimetry method based on GA-optimized quantum CS. In Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 4530-4535. (Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022). doi: 10.1109/CCDC55256.2022.10034143

Ultra-Quick pulsar positioning and velocimetry method based on GA-optimized quantum CS

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