Author(s)

XianYang Zhou^1*, WeiRong Zhang², SiQi Ji¹, Yang Song¹

Affiliation(s)

¹College of Arts and Sciences, Northeast Agricultural University, Harbin 150030, Heilongjiang, China.

²Guiyang No. 25 High School, Guiyang 550025, Guizhou, China.

Corresponding Author

XianYang Zhou

ABSTRACT

Aiming at the transonic sonic boom localization problem generated by the falling rocket debris, this paper proposes a spatio-temporal localization method of debris based on acoustic monitoring. The sonic boom signal is received by multiple monitoring devices, and the effects of time error and equipment layout on positioning accuracy are analyzed to construct a single target and multi-target cooperative positioning model. For single wreckage, the four-sphere intersection principle and the least-squares method with error compensation are adopted to realize the accurate solution of three-dimensional coordinates and sonic boom moment through four monitoring devices, and the localization results are 110.57°longitude, 27.16°latitude, 957.96 m elevation, and 100.753 s. For multi-wreckage scenarios, the classification model of vibration wave signals is proposed, and the classification model of vibration wave signals is proposed by combining the Pearson correlation coefficient and the K-means clustering algorithm. A vibration wave signal classification model is proposed, and a time difference threshold constraint (≤5s) is introduced to optimize the wreckage matching, which effectively solves the problem of overlapping multi-source sonic boom signals. The results of this paper show that the proposed method significantly improves the robustness and accuracy of rocket debris localization in complex scenes, and can provide theoretical support for the rapid confirmation of the drop point.

KEYWORDS

Rocket debris; Transonic sonic boom; Error compensation; Least squares; K-means

CITE THIS PAPER

XianYang Zhou, WeiRong Zhang, SiQi Ji, Yang Song. Least squares and cluster analysis based methodology for sonic boom localization of rocket debris. World Journal of Engineering Research. 2025, 3(1): 83-89. DOI: https://doi.org/10.61784/wjer3021.

REFERENCES

[1] Wang H. Research on mobile robot sound source localization method based on sound arrival time difference. Qingdao University of Science and Technology, 2020.

[2] Jin-Hong Xu. Three-dimensional localization method based on optimization principle in non-line-of-sight propagation environment. Zhejiang Gongshang University, 2018.

[3] Wang J T, Huang F Y, Li Z Z, et al. Research on indoor localization based on UWB ranging technology. Communication Power Technology, 2018, 35(05): 50-53+56.

[4] Wang Pei, Xu Shan, Xiong Wei, et al. Research on trajectory localization and monitoring technology of bullet and arrow wreckage based on TDOA. Fire and Command Control, 2023, 48(11): 139-144+151.

[5] An Jinxia, Qin Zhigang, Li Xuan, et al. Comprehensive situational analysis method and application of rocket debris landing site. Network Security and Data Governance, 2024, 43(11): 37-42.

[6] Chen Zhangrui. Reflection and analysis on launch vehicle recovery technology. Science and Technology Communication, 2019, 11(06): 140-142.

[7] Dai Yufan, Jin Hongping. Indoor localization method based on K-means and KNCN algorithm. Journal of Hubei Institute of Automobile Industry, 2024, 38(04): 59-63+68.

[8] Albert A, Alves S, André M, et al. Acoustic positioning for deep sea neutrino telescopes with a system of piezo sensors integrated into glass spheres. Experimental Astronomy, 2025, 59(1): 6.

[9] Fumiaki T, Motoyuki K. An approximate travel time calculation and a robust GNSS-acoustic positioning method using an MCMC technique. Earth, Planets and Space, 2022, 74(1).

[10] Geng Aoting. Research on key technology of multi-station passive localization based on AOA. Nanchang University, 2023.

[11] Wu P. Research on key technology of multi-station passive localization based on TDOA. National University of Defense Technology, 2019.

[12] Guo Jian, Zeng Zhihao, Huang Xihang. Secondary localization of mobile robot based on improved least squares method. Combined Machine Tools and Automated Machining Technology, 2024, (12): 62-66+71.

[13] Petrushka K, Petrushka I, Yukhman Y. Assessment of the Impact of Military Actions on the Soil Cover at the Explosion Site by the Nemerov Method and the Pearson Coefficient Case Study of the City of Lviv. Journal of Ecological Engineering, 2023.

[14] Xi Z L, Lu W. Application of Kmeans Algorithm with Pearson Coefficient in College Student Sports Quality and Sports Resources. Advanced Materials Research, 2014, 3078(905): 725-730.

[15] Zhang Xiaodong, Li Bo, Zhang Tiange, et al. Calculation and analysis of wind deviation of transmission lines based on K-mean clustering algorithm. Electrical Technology, 2023, 24 (12): 20-26+34.

[16] hemiakina A, Khrisanfov M, Chikurova N, et al. Getting Insights Into Chromatographic Properties of HILIС and Mixed‐Mode Homemade Stationary Phases Using Principal Component and Cluster Analyses. Journal of Chemometrics, 2025, 39(3): e70019.

[17] Díaz T M, Ruiz M A, Henríquez L P. Functional PCA and cluster analysis for determining temperature patterns in Chile. Theoretical and Applied Climatology, 2025, 156(3): 186.

[18] Mao Chunxian. Research on the method of solving system of super definite linear equations based on vector DSP. Hunan University of Science and Technology, 2023.