The chaos oscillation particle swarm optimization (COPSO) algorithm is prone to binge trapped in the local optima when dealing with certain complex models in ground-penetrating radar (GPR) data inversion, because it inherently suffers from premature convergence, high computational costs, and extremely slow convergence times, especially in the middle and later periods of iterative inversion. Considering that the bilateral connections between different particle positions can improve both the algorithmic searching efficiency and the convergence performance, we first develop a fast single-trace-based approach to construct an initial model for 2-D PSO inversion and then propose a TV-regularization-based improved PSO (TVIPSO) algorithm that employs total variation (TV) regularization as a constraint technique to adaptively update the positions of particles. B by adding the new velocity variations and optimal step size matrices, the search range of the random particles in the solution space can be significantly reduced, meaning blindness in the search process can be avoided. By introducing constraint-oriented regularization to allow the optimization search to move out of the inaccurate region, the premature convergence and blurring problems can be mitigated to further guarantee the inversion accuracy and efficiency. We report on three inversion experiments involving multilayered, fluctuated terrain models and a typical complicated inner-interface model to demonstrate the performance of the proposed algorithm. The results of the fluctuated terrain model show that compared with the COPSO algorithm, the fitness error (MAE) of the TVIPSO algorithm is reduced from 2.3715 to 1.0921, while for the complicated inner-interface model the fitness error (MARE) of the TVIPSO algorithm is reduced from 1.9539 to 1.5674.

Ground-penetrating radar (GPR) diffractions typically manifest weak responses resulting from electromagnetic wave interactions with subwavelength heterogeneities (e.g., cracks and voids) or geometric discontinuities (e.g., scattering edges and corners). These subtle yet informative wavefield components are critical for enabling enhanced illumination and dedicated imaging of subsurface microfeatures in infrastructure assessment and geological hazard mitigation. Current diffraction extraction methods in GPR face significant practical limitations: kinematic local-slope techniques are computationally intensive and may lead to loss of vertex events, whereas coherence analysis (CA) requires meticulous calibration of scaling factors and time-moveout corrections. Such constraints can compromise the resolution of imaging modalities, such as migration, tomography, and full-waveform inversion (FWI). To address these challenges, this study introduces an adaptive diffraction extraction workflow that integrates three core modules in wavelet and dip domains: a wavelet-based reflection model for structural preservation, dip-adaptive smoothing operators for wavefield decomposition, and particle swarm optimization (PSO) with multiobjective criteria (Laplacian sharpness, wavelet entropy (WE), and focusing metrics) for parameters optimization. The workflow iteratively extracts residual diffractions by optimizing wavelet decomposition levels, smoothing scales, and residual thresholds using PSO-driven fitness functions. Validated through the heterogeneous random media model, the Sigsbee2a benchmark, and field experiments, the PSO-guided workflow demonstrates quantifiable improvements in edge preservation, coherence artifacts reduction, spatial resolution, and kinematic consistency. By prioritizing the wavefield integrity and structural fineness, the proposed method provides refined inputs for subsequent imaging, highlighting its potential to advance the resolution and reliability of near-surface GPR sensing applications.

Nowadays, contactless vital sign (VS) reorganization has been used, so get more consideration and huge realizations. The electromagnetic reorganization is observed as the most undertaking techniques, that can acquire VS in the range-frequency matrix. Ultra-wide band (UWB) impulse radar used at most of the indoor object position detecting, VS reorganization, and also human fall reorganization using the continuous wave. As a better choice, UWB pulse radar has been generally utilized in movable object reorganization, through-wall imaging and post-earthquake look for and rescue because of its understanding benefits like mighty fitness and distinguished time determination.