Antinoise Robustness Scalable Joint Estimation of Frequency and DOA for Sparse Arrays

HUANG Xiang-dong; YANG Lin; YANG Meng-kai; HUANG Guang-ming

doi:10.3969/j.issn.0372-2112.2019.01.016

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Antinoise Robustness Scalable Joint Estimation of Frequency and DOA for Sparse Arrays

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- Antinoise Robustness Scalable Joint Estimation of Frequency and DOA for Sparse Arrays
- Acta Electronica Sinica Vol. 47, Issue 1, Pages: 122-128(2019)
- 作者机构：
  
  1. 天津大学电气自动化与信息工程学院,天津,300072
  2. 电子信息控制重点实验室,四川,成都,610036
  3. 天津大学电气自动化与信息工程学院,天津,300072
  4. 电子信息控制重点实验室,四川,成都,610036
- 作者简介：
- 基金信息：
  
  2017 Program of Key Laboratory of Electronic Information Control
- DOI：10.3969/j.issn.0372-2112.2019.01.016
  CLC： TN911.72
- Published Online：25 January 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
HUANG Xiang-dong, YANG Lin, YANG Meng-kai, et al. Antinoise Robustness Scalable Joint Estimation of Frequency and DOA for Sparse Arrays[J]. Acta Electronica Sinica, 2019, 47(1): 122-128.
DOI：

HUANG Xiang-dong, YANG Lin, YANG Meng-kai, et al. Antinoise Robustness Scalable Joint Estimation of Frequency and DOA for Sparse Arrays[J]. Acta Electronica Sinica, 2019, 47(1): 122-128. DOI： 10.3969/j.issn.0372-2112.2019.01.016.

摘要

为提高空时域欠采样下的入射信号的频率和到达角估计器的抗噪鲁棒性，本文提出从改善联合估计器的两个基本问题入手解决此难题.在阵元排列与配置方面，构造仅包含3个阵元的互素松弛阵列，并且根据面向鲁棒性余数系统要求配置阵元间距；在频率和到达角参数重构算法的设计方面，本文用面向鲁棒性余数系统的重构方法替换了中国余数定理重构算法，并对抗噪鲁棒性调节机理做了深入详细的分析.仿真结果表明：经过以上两方面根本性的改进，本文提出的抗噪鲁棒性可分级的联合估计器，在无需增加硬件复杂度和系统成本的前提下，无论在频率估计还是到达角估计方面，其抗噪鲁棒性的信噪比阈值改善均可达到9dB以上.因而在雷达、遥感等被动感知领域具有较广阔的应用前景.

Abstract

To improve the antinoise robustness of frequency and direction of arrival in the temporal-spatial undersampling case

this paper presents two aspects of improvements.On one hand

in the configuration of sparse array arrangement

this paper constructs a relaxed coprime sparse array consisting of 3 sensors

whose element spacings are configured in terms of TRRNS (Towards Robustness in Residue Number System) reconstruction algorithm;On the other hand

in the design of recovery algorithm

the original CRT (Chinese Remainder Theorem) based algorithm is replaced by the TRRNS algorithm

from which the mechanism of the anti-noise robustness scalable adjustment will be derived and verified by numerical simulations.Compared to the original CRT based joint estimator

the proposed estimator at least achieves 9dB improvement of the SNR threshold without increasing the hardware complexity and system cost

which presents vast applications in radar

remote sensing and other passive sensing fields.

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