基于穩(wěn)定分布噪聲稀疏性及最優(yōu)匹配的跳頻信號參數(shù)估計

金艷; 武艷鳳; 姬紅兵

doi:10.11999/JEIT161397

基于穩(wěn)定分布噪聲稀疏性及最優(yōu)匹配的跳頻信號參數(shù)估計

doi: 10.11999/JEIT161397

基金項目:

國家自然科學(xué)基金(61201286)，陜西省自然科學(xué)基金(2014JM8304)和中央高校基本科研業(yè)務(wù)費專項資金(K5051202013)

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- 被引次數(shù): 0
出版歷程
- 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: 目前基于壓縮感知的跳頻信號參數(shù)估計方法大多是在高斯背景噪聲下進行的研究，而在非高斯穩(wěn)定分布脈沖噪聲環(huán)境下，已有基于高斯噪聲數(shù)學(xué)模型設(shè)計的算法性能下降。針對上述問題，該文分析了穩(wěn)定分布噪聲的大幅值脈沖滿足近似稀疏性條件，利用跳頻信號與噪聲之間的時域特征差異將信噪分離，實現(xiàn)噪聲抑制。并在壓縮感知框架下，建立與跳頻信號特點相匹配的3參數(shù)字典，采用最優(yōu)匹配(Optimal Match, OM)方法對跳頻信號自適應(yīng)分解，獲取匹配原子，基于這些時頻原子包含的信息估計跳頻信號的參數(shù)。仿真驗證表明，在穩(wěn)定分布噪聲中，與常規(guī)的跳頻信號估計方法相比，該文提出的先利用噪聲稀疏性去噪，再采用最優(yōu)匹配提取跳頻信號參數(shù)的方法(Sparsity-OM, SOM)，能夠較好地抑制脈沖噪聲，獲得準確的參數(shù)信息，具有良好的魯棒特性。
- 跳頻信號 /
- 參數(shù)估計 /
- 壓縮感知 /
- 噪聲稀疏性 /
- 最優(yōu)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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