基于稳定分布噪声稀疏性及最优匹配的跳频信号参数估计

金艳; 武艳凤; 姬红兵

doi:10.11999/JEIT161397

基于稳定分布噪声稀疏性及最优匹配的跳频信号参数估计

doi: 10.11999/JEIT161397

基金项目:

国家自然科学基金(61201286)，陕西省自然科学基金(2014JM8304)和中央高校基本科研业务费专项资金(K5051202013)

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出版历程
- 收稿日期: 2016-12-29
- 修回日期: 2017-06-14
- 刊出日期: 2017-10-19

Parameter Estimation of FH Signals Based on Stable Noise Sparsity and Optimal Match

Funds:

The National Natural Science Foundation of China (61201286), The Natural Science Foundation of Shaanxi Province (2014JM8304), The Fundamental Research Funds for the Central Universities (K5051202013)

摘要

摘要: 目前基于压缩感知的跳频信号参数估计方法大多是在高斯背景噪声下进行的研究，而在非高斯稳定分布脉冲噪声环境下，已有基于高斯噪声数学模型设计的算法性能下降。针对上述问题，该文分析了稳定分布噪声的大幅值脉冲满足近似稀疏性条件，利用跳频信号与噪声之间的时域特征差异将信噪分离，实现噪声抑制。并在压缩感知框架下，建立与跳频信号特点相匹配的3参数字典，采用最优匹配(Optimal Match, OM)方法对跳频信号自适应分解，获取匹配原子，基于这些时频原子包含的信息估计跳频信号的参数。仿真验证表明，在稳定分布噪声中，与常规的跳频信号估计方法相比，该文提出的先利用噪声稀疏性去噪，再采用最优匹配提取跳频信号参数的方法(Sparsity-OM, SOM)，能够较好地抑制脉冲噪声，获得准确的参数信息，具有良好的鲁棒特性。
- 跳频信号 /
- 参数估计 /
- 压缩感知 /
- 噪声稀疏性 /
- 最优匹配
Abstract: Currently, FH signal parameter estimation methods based on compressed sensing are mostly under the assumption of Gaussian noise background. In non-Gaussianstable distribution noise conditions, the algorithms based on Gaussian noise model suffer undesirable performance degradation. In this paper, it is analyzed and concluded that the spike pulses of the stable noise approximately meet sparse conditions. By using the differences of the characteristics in the time domain, the FH signal and the noise can be easily separated, and the goal of suppressing noise can be achieved. Under the framework of compressed sensing, the three-parameter dictionary is constructed based on the characteristics of FH signals, then the Optimal Match (OM) for adaptive FH signal decomposition is used to obtain the matching atoms and the FH signal parameters are estimated based on the information contained by these time frequency atoms. Simulation results show that compared with the conventional FH signal parameter estimation methods, the proposed Sparsity-OM (SOM), which uses noise sparsity to suppress the noise and then adopts the OM algorithm, improves the estimation accuracy of FH signal parameters and it is more robust to the stable distribution noise.
- Frequency Hopping (FH) signal /
- Parameter estimation /
- Compressed Sensing (CS) /
- Noise sparsity /
- Optimal Match (OM)

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参考文献(32)

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