Time-Delay Estimation by a Modified Orthogonal Matching Pursuit Method for Rough Pavement

Pan, Jingjing; Sun, Mingjian; Wang, Yide; Bastard, Cédric Le; Baltazart, Vincent

doi:10.1109/tgrs.2020.3006509

Cited by 9 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, compressive sensing (CS)-based methods have also been proposed for TDE by GPR, which can solve the issue of the highly correlated or coherent backscattered echoes without decorrelating. The matching pursuit (MP) and orthogonal matching pursuit (OMP) are the two most popular classical greedy algorithms for sparse reconstruction, which can be used for TDE [13]. However, these two methods cannot handle overlapped echoes (B∆τ < 1) due to limited resolution.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Time Delay Estimation Using Ground Penetrating Radar

et al. 2023

Self Cite

View full text Add to dashboard Cite

Time delay estimation (TDE) is of great interest for the thickness estimation of pavement using ground penetrating radar (a non-destructive testing tool that uses electromagnetic waves to probe civil engineering material), which determines the difference between the times of arrival of two incoming signals or backscattered echoes. However, conventional TDE methods suffer performance degradation because of limited resolution for thin layers and highly correlated backscattered echoes. In this paper, a deep neural network (DNN)-based TDE method is proposed. Firstly, a new DNN is constructed to classify and train the backscattered echoes; then, the time delays of the backscattered echoes can be estimated through the proposed DNN. The proposed method is based on the data processing of the backscattered echoes, which is more robust to the noise than conventional subspace-based methods (MUSIC, ESPRIT) and compressive sensing-based methods (OMP). The proposed method can directly process coherent backscattered echoes without decorrelation procedures, compared with MUSIC and ESPRIT. In addition, the proposed method is more powerful in resolving the close backscattered echoes than that of OMP. Simulation results show the efficiency of the proposed method in terms of signal-to-noise ratio (SNR) and BΔτ products. (The BΔτ products indicate the resolution of GPR, B is the frequency bandwidth of GPR and Δτ is the time delay between two incoming signals or backscattered echoes).

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Time Delay Estimation Using Ground Penetrating Radar

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, compressive sensing based methods, or sparse representation based methods, exploit the sparsity of signals in the interpretation of GPR data. In [22], [23], second order cone programming and orthogonal matching pursuit (OMP) are applied in the thickness estimation of pavement, and achieve good results in time-delay estimation (TDE). Unlike subspace based methods, compressive sensing based methods allow direct processing of coherent signals without applying spatial smoothing.…”

Section: Introductionmentioning

confidence: 99%

Time-Delay Estimation by Enhanced Orthogonal Matching Pursuit Method for Thin Asphalt Pavement With Similar Permittivity

Sun

Pan

Wang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

Time-delay estimation (TDE) for thin top layers of asphalt pavement is a challenging task due to the limited resolution of ground penetrating radar (GPR) as well as small permittivity difference between top layers. Echoes backscattered from the interfaces of top layers with similar permittivity have usually much smaller amplitudes compared with other echoes, which can be called weak signals. The weak backscattered echoes are usually too sensitive to the noise and other strong echoes that current signal processing approaches (subspace-based methods and compressed sensing based methods) might have false estimation results even failures without proper processing of them. Therefore, in this paper, an enhanced orthogonal matching pursuit (OMP) method is proposed to deal with weak signals resulting from similar permittivity of adjacent asphalt layers. Based on the orthogonality between signal and noise subspaces, we firstly apply the truncated singular value decomposition (SVD) on the received signals, in order to reduce the noise impact. Secondly, we build an orthogonal matrix to the mode matrix of the pre-estimated strong backscattered echoes, and map it to the overcomplete dictionary matrix, such that the influence of the residual of the strong backscattered echoes can be reduced. Finally, the time-delays of backscattered echoes and layer thicknesses are estimated. Compared with conventional approaches, the proposed method is more suitable for TDE in thin asphalt pavement detection. The accuracy of the proposed method is validated by both numerical and experimental data.

show abstract

Improving the Ability of Measuring the Thickness of Thin Oil Slick With Ultrasound Using the Sparse Optimization Method

Yao,

Du,

et al. 2024

IEEE Sensors J.

View full text Add to dashboard Cite

Time-Delay Estimation by a Modified Orthogonal Matching Pursuit Method for Rough Pavement

Cited by 9 publications

References 36 publications

Deep Learning-Based Time Delay Estimation Using Ground Penetrating Radar

Deep Learning-Based Time Delay Estimation Using Ground Penetrating Radar

Time-Delay Estimation by Enhanced Orthogonal Matching Pursuit Method for Thin Asphalt Pavement With Similar Permittivity

Improving the Ability of Measuring the Thickness of Thin Oil Slick With Ultrasound Using the Sparse Optimization Method

Contact Info

Product

Resources

About