基于<em>α</em>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

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Abstract Currently, FH signal parameter estimation methods based on compressed sensing are mostly under the assumption of Gaussian noise background. In non-Gaussian α stable 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 α 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 α stable distribution noise.

Key words： Frequency Hopping (FH) signal Parameter estimation Compressed Sensing (CS) Noise sparsity Optimal Match (OM)

Received: 29 December 2016 Published: 21 July 2017

PACS:

TN911.7

Fund: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)

Corresponding Authors: WU Yanfeng E-mail: 1374754117@qq.com

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Cite this article:

JIN Yan,WU Yanfeng,JI Hongbing. Parameter Estimation of FH Signals Based on α Stable Noise Sparsity and Optimal Match[J]. JEIT, 2017, 39(10): 2413-2420.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT161397 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I10/2413

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