摘 要
随着现代通信技术的快速发展，复杂电磁环境对通信信号质量提出了更高要求，数字信号处理技术成为提升信号传输性能的重要手段。本研究旨在探索基于数字信号处理的通信信号增强方法，以应对噪声干扰和信道失真等问题。通过引入自适应滤波算法、小波变换降噪以及深度学习模型，提出了一种综合优化的信号增强框架。该框架能够动态调整滤波参数，有效分离目标信号与背景噪声，并在非平稳信道条件下保持较高的信号保真度。实验结果表明，所提方法在低信噪比环境下显著提升了信号的清晰度和可辨识性，相较于传统方法，误码率降低了约30%。此外，创新性地将深度神经网络应用于非线性信道补偿，进一步提高了系统的鲁棒性和适应性。本研究为复杂通信场景下的信号增强提供了新思路，具有重要的理论意义和实际应用价值。
关键词：数字信号处理；通信信号增强；自适应滤波
Abstract
With the rapid development of modern communication technology, the complex electromagnetic environment has imposed higher requirements on the quality of communication signals, making digital signal processing (DSP) techniques a critical approach to enhancing signal transmission performance. This study focuses on exploring DSP-based methods for communication signal enhancement to address issues such as noise interference and channel distortion. By incorporating adaptive filtering algorithms, wavelet transform denoising, and deep learning models, a comprehensively optimized signal enhancement fr amework is proposed. This fr amework can dynamically adjust filtering parameters, effectively separate target signals from background noise, and maintain high signal fidelity under non-stationary channel conditions. Experimental results demonstrate that the proposed method significantly improves signal clarity and recognizability in low signal-to-noise ratio environments, reducing the bit error rate by approximately 30% compared to traditional approaches. Additionally, this study innovatively applies deep neural networks to nonlinear channel compensation, further enhancing the robustness and adaptability of the system. This research provides new insights into signal enhancement in complex communication scenarios, offering significant theoretical implications and practical application value.
Keywords: Digital Signal Processing；Communication Signal Enhancement；Adaptive Filtering
目 录
引言 1
一、数字信号处理基础研究 1
（一）数字信号处理基本原理 1
（二）通信信号的特性分析 1
（三）信号增强的关键技术 2
二、通信信号增强方法概述 2
（一）噪声对通信信号的影响 2
（二）传统增强方法的局限性 3
（三）数字信号处理的优势 3
三、基于滤波器的信号增强技术 4
（一）滤波器的基本原理与分类 4
（二）自适应滤波器在信号增强中的应用 4
（三）滤波器优化设计的研究 5
四、高级数字信号增强算法研究 5
（一）小波变换在信号增强中的作用 5
（二）神经网络算法的应用与改进 5
（三）复杂环境下的信号增强策略 6
结 论 6
致 谢 8
参考文献 9
