基于分数低阶类相关熵的双基地MIMO雷达目标参数联合估计新算法

李丽; 邱天爽; 贺明妍

doi:10.11959/j.issn.1000-436x.2016236

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基于分数低阶类相关熵的双基地MIMO雷达目标参数联合估计新算法

学术论文 | 更新时间：2024-06-05

- 基于分数低阶类相关熵的双基地MIMO雷达目标参数联合估计新算法
- Novel method based on fractional lower-order correntropy-analogous statistics for parameter jointly estimation in bistatic MIMO radar
- 通信学报 2016年37卷第12期页码：42-49
- 作者机构：
  
  1. 大连大学信息工程学院，辽宁大连 116622
  2. 大连理工大学电子信息与电气工程学部，辽宁大连 116024
- 作者简介：
  
  [ "李丽（1979-），女，满族，黑龙江佳木斯人，博士，大连大学副教授，主要研究方向为阵列信号处理、参数估计。" ]
  [ "邱天爽（1954-），男，江苏海门人，博士，大连理工大学教授、博士生导师，主要研究方向为数字信号处理理论及应用等。" ]
  [ "贺明妍（1981-），女，山东烟台人，博士，大连大学讲师，主要研究方向为通信信号处理、高斯信号处理。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61401055);国家自然科学基金资助项目(61671105)
- DOI：10.11959/j.issn.1000-436x.2016236
  中图分类号： TN911.7
- 网络出版日期：2016-12，
  
  纸质出版日期：2016-12-25
- 稿件说明：
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李丽, 邱天爽, 贺明妍. 基于分数低阶类相关熵的双基地MIMO雷达目标参数联合估计新算法[J]. 通信学报, 2016,37(12):42-49.

Li LI, Tian-shuang QIU, Ming-yan HE. Novel method based on fractional lower-order correntropy-analogous statistics for parameter jointly estimation in bistatic MIMO radar[J]. Journal on communications, 2016, 37(12): 42-49.
李丽, 邱天爽, 贺明妍. 基于分数低阶类相关熵的双基地MIMO雷达目标参数联合估计新算法[J]. 通信学报, 2016,37(12):42-49. DOI： 10.11959/j.issn.1000-436x.2016236.

Li LI, Tian-shuang QIU, Ming-yan HE. Novel method based on fractional lower-order correntropy-analogous statistics for parameter jointly estimation in bistatic MIMO radar[J]. Journal on communications, 2016, 37(12): 42-49. DOI： 10.11959/j.issn.1000-436x.2016236.

摘要

针对Alpha稳定分布噪声环境下参数估计性能退化的问题，受类相关熵概念的启发，提出分数低阶类相关熵（FCAS）的概念，并采用分数低阶类相关熵准则对平行因子分析(PARAFAC)算法中基于三线性最小二乘（TALS）迭代准则的目标函数进行了修正，推导出适用于冲激噪声环境的韧性平行因子分析（FCAS-PARAFAC）算法，并将该方法应用于双基地MIMO雷达系统中目标参数估计中。FCAS-PARAFAC算法能够抑制脉冲噪声的影响，具有较好的估计性能，并且能够实现自动配对，仿真实验验证了算法的有效性。

Abstract

According to the performance degradation problem of parameter estimation algorithm in the Alpha stable dis-tribution noise

inspired by the concept of correntropy

a new class of statistics

namely

the fractional lower-order cor-rentropy-analogous statistics (FCAS) was proposed. By employing the fractional lower-order correntropy-analogous sta-tistics based cost function in parallel factor (PARAFAC)

the FCAS-PARAFAC algorithm was deduced which can be utilized for the parallel factor under impulsive noise environments. The FCAS-PARAFAC algorithm was applied to pa-rameter estimation in bistatic MIMO radar under impulsive noise environment. The proposed method can suppress the impulse noise interference and has better estimation performance. Furthermore

the estimated parameters are automati-cally paired without the additional pairing method. Simulation results are presented to verify the effectiveness of the pro-posed method.

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references

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