| 摘要: |
| 提出了一种适用于低信噪比情况下提取扩频信号特征参数的算法。该算法通过对被测信号的多次采样、分段累乘，扩大了待分解信号的样本数，降低了噪声的影响，从而能够获得比传统子空间分解算法更好的性能。通过分段累乘构造的自相关矩阵，对其进行特征值分解后，表现出对噪声不敏感的特性，在一定程度上克服了常规方法的噪声敏感缺点。对算法的仿真计算表明，该方法应用在低信噪比的通信环境下，信号特征值不会被噪声湮没，解决了传统子空间方法在低信噪比条件下的分辨率不足的问题。该算法的提出对低信噪比条件下的扩频信号处理和参数检测有重要的工程和实际意义。 |
| 关键词: 直接序列扩频信号 特征值分解 子空间理论 相关矩阵 |
| DOI： |
|
| 基金项目:国家自然科学基金资助项目(61271168) |
|
| Subspace auto-correlation matrix construction based on matrix multiplication |
| CHENG Hao,LIU Jun |
| () |
| Abstract: |
| An algorithm for extracting characteristic parameters of spread spectrum signal is proposed,which is suitable for low signal to noise ratio(SNR) condition.The algorithm expands the number of samples of the signal to be decomposed and reduces the effect of noise,so as to obtain better performance than the traditional subspace decomposition algorithm.By the auto correlation matrix of the block structure, the characteristics of the noise are not sensitive to the characteristic values,and the noise sensitivity of the conventional method is overcome to some extent.The simulation results show that when the method is applied to the communication environment of low SNR,the signal characteristic value is not lost. The resolution of the traditional subspace method in low SNR condition is solved.The proposed algorithm has important engineering and practical significance for the signal processing and parameter detection in low SNR condition. |
| Key words: DSSS signal eigen value decomposition subspace theory correlation matrix |
《电讯技术》编辑部 