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基于自适应参数估计的微动时频表征重构方法

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针对数据缺失条件下的目标微动回波时频表征重构问题,提出了一种基于自适应参数估计的微动时频表征重构方法.首先,将缺失的微动时频表征重构问题建模为基于LP范数最小化的稀疏重构问题,其次,引入哈达玛积参数将Lp范数最小化稀疏重构问题转化为多个L2范数联合最小化问题,并采用迭代吉洪诺夫正则化求解,同时在每次迭代过程中根据重构结果自适应估计正则化参数,最后,采用除偏处理减小了重构时频表征的振幅衰减.与传统微动回波时频表征重构方法相比,所提方法避免了需要人工设置正则化参数不足的问题,并且重构的时频表征更加完整.仿真实验和实测数据处理结果验证了所提方法的有效性和稳健性.
A Reconstruction Method of Micro-Motion TFR Based on Adaptive Parameter Estimation
In view of the time-frequency representation(TFR)reconstruction of the micro-motion signals under conditions of incomplete data,a reconstruction method of micro-motion is proposed based on the adaptive parame-ter estimation.Firstly,the missing micro-motion TFR reconstruction problem is modeled on the Lp norm minimi-zation sparse reconstruction problem,and by introducing Hadamard product parameter(HPP),the Lp norm min-imization sparse reconstruction problem is transferred to a joint minimization problem with multiple L2 norm,and solved by using iterative Tikhonov regularization.Simultaneously,the regularization parameter is estimated adap-tively in each iteration based on reconstruction results.Finally,the amplitude decay of the reconstructed TFR is reduced by the de-biasing process.Compared with the traditional micro-motion echo time-frequency representation reconstruction method,the proposed method avoids the disadvantage of setting the regularization parameter manu-ally,and the reconstructed TFR is more complete.The effectiveness and robustness of the proposed method is verified by simulation and measured data processing.

micro-Doppler effectsignal reconstructionsparse optimizationparameter estimationtime-frequency analysis

李开明、王欢、解岩、陈卓、高泽岳

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空军工程大学信息与导航学院,西安,710077

信息感知技术协同创新中心,西安,710077

西安电子工程研究所总体二部,西安,710100

四川省政府服务和公共资源交易服务中心,成都,610000

95894部队,北京,112211

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微多普勒效应 信号重构 稀疏优化 参数估计 时频分析

国家自然科学基金面上项目国家自然科学基金面上项目国家自然科学基金面上项目国家自然科学基金面上项目

62371468623015996227150062131020

2024

空军工程大学学报
空军工程大学科研部

空军工程大学学报

CSTPCD北大核心
影响因子:0.55
ISSN:2097-1915
年,卷(期):2024.25(5)
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