Multi-radar data fusion for maritime moving target detection based on three-dimensional sliding window

Wen, He; Li, Yu; Duan, Chongdi; Zhang, Jun; Li, Ning; Wu, Tao

doi:10.1080/01431161.2023.2168137

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Xiao et al [19] developed a signal fusion method to extrapolate a high-frequency, high-resolution GPR signal at a greater depth based on a lower-frequency, greater-penetration signal using extrapolation with a deterministic deconvolution algorithm. Wen et al [20] presented a multi-station fusion detection method for a moving maritime moving target based on a three-dimensional sliding window.…”

Section: Introductionmentioning

confidence: 99%

Multi-Frequency GPR Data Fusion through a Joint Sliding Window and Wavelet Transform-Weighting Method for Top-Coal Structure Detection

Guan,

Liu

2024

Applied Sciences

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Top-coal structure detection is an important basis for realizing effective mining in fully mechanized cave faces. However, the top-coal structure is very complex and often contains multi-layer gangues, which seriously influence the level of effective mining. For these reasons, this paper proposes a novel multi-frequency ground-penetrating radar (GPR) data-fusing method through a joint sliding window and wavelet transform weighting method to accurately detect the top-coal structure. It possesses the advantages of both high resolution and great detection depth, and it can also integrate multi-frequency GPR data into one composite profile to interpret the internal structure information of top coal in detail. The detection procedure is implemented following several steps: First of all, the multi-frequency GPR data are preprocessed and aligned through a band-pass filter and a zero offset elimination method to establish their spatial correspondences. Secondly, the proposed method is used to determine the time-varying weight values of each frequency GPR signal according to the wavelet energy proportion within the sliding window; also, the edge detection algorithm is introduced to improve the fusion efficiency of the wavelet transform so as to realize the effective fusion of the multi-frequency GPR data. Thirdly, a reflection intensity model of multi-frequency GPR signals traveling in the top-coal is established by using the stratified identification method, and then, the detailed top-coal structure can be inversely interpreted. Finally, the quantitative evaluation criteria, information entropy (IE), space–frequency (SF) and Laplacian gradient (LG), are used to evaluate the multi-frequency GPR data fusion’s effectiveness in laboratory and field environments. The experimental results show that, compared with the genetic, time-varying and wavelet transform fusion method, the fusion performance of the presented method possesses higher values in the IE, SF and LG evaluation criteria, and it also has both the merits of high resolution and great detection depth.

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Section: Introductionmentioning

confidence: 99%

Multi-Frequency GPR Data Fusion through a Joint Sliding Window and Wavelet Transform-Weighting Method for Top-Coal Structure Detection

Guan,

Liu

2024

Applied Sciences

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“…Recent studies have utilized satellite systems as external radiation sources for target detection. Satellites are advantageous in this regard due to their distance from the ground, which makes it easier to approximate the Doppler frequency rate [18][19][20][21]. Duan [19] constructed a pre-compensation function using the range and Doppler of the beam center pointing cell, which mitigated the Doppler frequency rate caused by different observation configurations.…”

Section: Introductionmentioning

confidence: 99%

“…Duan [19] constructed a pre-compensation function using the range and Doppler of the beam center pointing cell, which mitigated the Doppler frequency rate caused by different observation configurations. In a study conducted by Wen [21], the focus was on examining the acquisition of multi-station echoes in the Cartesian Doppler frequency rate domain. The study explored a single transmission scenario and proposed the design of a three-dimensional sliding window.…”

Section: Introductionmentioning

confidence: 99%

A Novel Target Detection Method Based on Multi-Parameter Space for Mobile Passive Multistatic Radar

Zhang,

Liu,

Dong

et al. 2023

Remote Sensing

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Deploying Passive Multistatic Radar (PMR) on mobile platforms provides covert and cost-effective monitoring over a large area, offering certain advantages in countermeasure. However, mobile PMR faces significant challenges, such as Doppler distortion and phase deviations. A multi-parameter space target detection method is proposed for mobile PMR to achieve target detection in three-dimensional environments. By estimating the Doppler Frequency Rate (DFR), applying bistatic range phase compensation, and implementing azimuth time integration, frame division, and data fusion, the detection accuracy and the Signal-to-Noise Ratio (SNR) are improved. Simulation results indicate that the proposed method significantly enhances the SNR and produces accurate detection results, demonstrating its efficacy.

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Enhanced-YOLOv8: A new small target detection model

Wei,

Tong

2024

Digital Signal Processing

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Multi-radar data fusion for maritime moving target detection based on three-dimensional sliding window

Cited by 4 publications

References 17 publications

Multi-Frequency GPR Data Fusion through a Joint Sliding Window and Wavelet Transform-Weighting Method for Top-Coal Structure Detection

Multi-Frequency GPR Data Fusion through a Joint Sliding Window and Wavelet Transform-Weighting Method for Top-Coal Structure Detection

A Novel Target Detection Method Based on Multi-Parameter Space for Mobile Passive Multistatic Radar

Enhanced-YOLOv8: A new small target detection model

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