Radar Detection Technology For Visual Blind Area Targets In Architectural Environment

Architectural environment visual blind zone target radar detection technology refers to the detection,positioning,and identification of targets in the blind zones of radar detection in architectural environments.In these blind zones,targets are concealed from the radar’s line of sight due to obstruction by buildings and obstacles,causing the direct electromagnetic propagation path from the radar to the target to disappear.This technology utilizes multiple reflections between buildings and diffraction at corners to detect,locate,and identify targets in visual blind zone environments.It holds significant application prospects in counter-terrorism,disaster relief,intelligent driving,and other fields.However,the main challenge and focus of this technology lie in how to utilize reflection and diffraction information and extract useful information from the received multipath signals to ultimately achieve target detection,positioning,and identification.The dissertation focuses on radar detection technology for targets in architectural environment visual blind zones,conducting research on weak vital signs detection under visual blind zones,obscured moving target localization,and human behavior recognition in obstructed environments.The main contributions and innovations are as follows:1.Analysis of micro-motion target detection principles in non-line-of-sight visual blind zones,establishment of a weak target vital signs signal model in non-line-of-sight visual blind zones,and introduction of an adaptive correlation entropy-based vital signs parameter estimation method.This method enhances the signal-to-noise ratio of the received signal through adaptive correlation entropy,addressing the false alarm and missed detection issues caused by traditional peak detection algorithms,and achieving static target parameter estimation.2.Proposed an algorithm for non-line-of-sight target multi-path respiration detection based on Empirical Mode Decomposition(EMD),where each path signal is processed along the time dimension using the EMD algorithm to ultimately obtain the human respiratory frequency,enabling non-line-of-sight human detection.To further obtain the respiratory and heartbeat frequencies of human targets,an algorithm for high-precision vital sign estimation based on multi-path correlation and cross-power spectral estimation is proposed.The Minimum Variance Distortionless Response(MVDR)algorithm is employed to obtain the position of non-line-of-sight targets,followed by estimation of the target’s respiratory and heartbeat parameters through multi-path cross-power spectral analysis.3.Proposal of a non-line-of-sight target localization algorithm based on dual-view multi-imaging dictionary.Initially,a single-channel radar non-line-of-sight electromagnetic propagation model is established,and single-radar target imaging is achieved through the construction of different multipath lookup tables.Then,energy redistribution of different radar perspectives is performed,followed by dual-view imaging to achieve target localization in non-line-of-sight scenarios and multipath ghost suppression.4.Proposal of a method for incomplete building layout information and non-line-ofsight target localization based on hypothesis matching.In unknown building scenarios,utilizing the coupling relationship between ghost positions,building layouts,and multipath distances,building layout and target position joint estimation is achieved through the hypothesis matching method.5.Proposal of a non-line-of-sight human behavior recognition method based on multipath distance information fusion.This method designs a depth feature extraction module to analyze distance image characteristics and a multipath information fusion module to solve the problem of weak correlation with broad distance feature distribution,achieving non-line-of-sight target behavior recognition and improving recognition accuracy.6.Proposal of a lightweight human behavior recognition method in non-line-of-sight scenarios.This method constructs a lightweight human behavior recognition module with multi-scale convolution units and a lightweight Doppler feature extraction module,and combines the two modules to establish a multi-class spectrogram human behavior recognition model,achieving non-line-of-sight target behavior recognition with significantly improved recognition rates compared to state-of-the-art recognition models.All the proposed models and methods have been experimentally validated through electromagnetic simulation and self-developed radar experiments,demonstrating the effectiveness and accuracy of the established models and proposed algorithms.