University of Leicester
2019HONGYUANJPhD.pdf (6.42 MB)

Landmine Detection Algorithm Design Based on Data Fusion Technology

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posted on 2018-12-12, 13:43 authored by Hongyuan Jing
This research has focused on close-in landmine detection, which aims to identify landmines in a particular landmine area. Close-range landmine detection requires both sub-surface sensors, such as metal detectors and ground penetrating radar (GPR), and surface sensors, such as optical cameras. A new multi-focus image fusion algorithm is proposed which outperforms the existing intensity-hue-saturation (IHS) and principle components analysis (PCA) algorithms on both visual and fusion parameter analysis. In addition, the proposed algorithm can save 30.9% running time than the IHS algorithm, which is the same level as the existing PCA algorithm. A novel single GPR sensor landmine detection algorithm entropy-based region selecting algorithm is proposed which uses the entropy value of the region as the feature and continuous layers instead of a hard threshold. Two A-scan based statistics algorithms and a GPR signal oscillation feature based detection algorithm are also proposed. The results show that the proposed entropy-based algorithm outperforms the existing region selection algorithm on both detection accuracy and running time. The proposed statistics algorithms and GPR feature-based algorithm outperform the edge histogram descriptor and edge energy algorithms on both detection accuracy range, running time and memory usage. In addition, the GPR feature-based algorithm can reduce the false alarm rate (FAR) by 22% for all targets at 90% probability of detection. With regards to data fusion system design, this research overcomes the limitations of the existing Bayesian fusion approach. A new Kalman-Bayes based fusion system is developed which reduces the system uncertainty and improves the fusion process. The experimental results have shown that the proposed Kalman-Bayes fusion system and enhanced fuzzy fusion system can reach 7.8% FAR at 91.1% detection rate and 6.30% FAR at 92.4% detection rate, correspondingly, outperforming the existing Bayes and fuzzy fusion systems in terms of detection ability.



Vladimirova, Tanya

Date of award


Author affiliation

Department of Engineering

Awarding institution

University of Leicester

Qualification level

  • Doctoral

Qualification name

  • PhD



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