在地下目标成像中，由于介质非均匀性和边界效应等问题，传统的成像方法往往存在局限性，影响了成像效果和目标检测的可靠性。相比于传统的成像方法，时间反演技术能够实现目标的精确定位并具有较高的成像分辨率，因此有着极大的研究价值。

时间反演由于其良好的自适应空时聚焦能力而被引入到电磁探测领域并应用于探地雷达目标成像，针对现有的电磁时间反演算子分解法（Diagonal Reassignment Optimal Reduced Technique，DORT）成像在复杂环境下分辨率较低而时间反演多信号分类法（Time Reversal-MUltiple SIgnal Classification，TR-MUSIC）受噪声干扰的缺点，对传统多重信号分类（TR-MUSIC）算法的优化。利用时间反演技术对电磁场数据进行空间平滑处理，实现对介质非均匀性的校正和成像模糊的补偿；采用构建加权矩阵方法，提高非理想环境中对目标信号估计的稳定性；通过两次空间平滑算法的叠加增强了数据预处理对原始信号数据的利用率，提高了波达方向估计的准确性。本文改进后的IWSS（Improve Weighted Spatial Smoothing，IWSS）算法相较其他文献中的算法在相同快照数、信噪比条件下，在保证波达方向估计精度的同时能利用更少的阵元个数完成更多的入射信号的波达方向估计，产生的峰值点与真实峰值点之间的误差不超过0.5°，对相干信号的均方根误差对比其他改进算法，在小快拍数和低信噪比的条件下分别提高了15.5%和41.7%，低信噪比情况下分辨率提高了0.27m，结合时间反演技术后的TR-IWSS算法具有较高的成像分辨率和更好的抗干扰能力，可以适应高噪声环境下的成像需求。

本文进行对基于时间反演的目标成像算法的改进，所研究的TR-IWSS算法通过对不同的环境参数进行仿真成像，对比其他文献算法，表明本文算法的优势，可以为地埋目标成像等相关研究提供一定的参考。

In underground target imaging, traditional imaging methods often have limitations due to media non-uniformity and boundary effects, which affect the imaging effect and reliability of target detection. Compared to traditional imaging methods, time inversion technology can achieve precise target localization and has higher imaging resolution, thus having great research value.

Time inversion has been introduced into the field of electromagnetic detection and applied to ground penetrating radar target imaging due to its excellent adaptive spatiotemporal focusing ability. In response to the low resolution of the existing Diagonal Reassignment Optimal Reduced Technique (DORT) imaging in complex environments, while Time Reversal Multiple Signal Classification (TR-MUSIC) is affected by noise interference, this thesis optimizes the traditional TR-MUSIC algorithm. Using time inversion technology to perform spatial smoothing on electromagnetic field data, achieving correction of medium non-uniformity and compensation for imaging blur; Using the method of constructing weighted matrices to improve the stability of target signal estimation in non ideal environments; The superposition of two spatial smoothing algorithms enhances the utilization of raw signal data in data preprocessing and improves the accuracy of direction of arrival estimation. The improved IWSS (Improved Weighted Spatial Smoothing, IWSS) algorithm in this article, compared to other improved algorithms in the same number of snapshots and signal-to-noise ratio conditions, can ensure the accuracy of direction of arrival estimation while using fewer array elements to complete more direction of arrival estimation of the incident signal. The error between the generated peak point and the true peak point does not exceed 0.5 °. Compared with other improved algorithms, the root mean square error of coherent signals is increased by 15.5% and 41.7% respectively under small snapshot numbers and low signal-to-noise ratio conditions, and the resolution is improved by 0.27m under low signal-to-noise ratio conditions. The TR-IWSS algorithm combined with time inversion technology has... Higher imaging resolution and better anti-interference ability can meet the imaging needs in high noise environments.

This article improves the target imaging algorithm based on time inversion. The TR-IWSS algorithm studied simulates imaging of different environmental parameters and compares it with other literature algorithms, indicating the advantages of this algorithm. It can provide a certain reference for related research such as buried target imaging.

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