多跳频信号频率跟踪与二维波达方向实时估计算法

doi:10.11999/JEIT151170

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Abstract

In order to extract Frequency-Hopping (FH) communication parameters and provide the necessary information for the communication countermeasure, an online estimation algorithm of 2D-DOA and frequency tracking for multiple FH signals is proposed in this paper. Firstly, the data model of the L-array for FH signals is built and the applicability of Auto Regresive Moving Average (ARMA) model to L-array data is proved. Then, the particle filtering is introduced to conduct the online estimation of manifold matrix and the frequency, and the ARMA model is built based on the frequency estimates, depending on which, the online detection of hop timing is obtained. After that , the precise estimation of 2D-DOA can be gained via manifold matrix estimates and without parameter matching. With the rational method of particle generation and the weight updating, the new method makes the estimates of manifold matrix and the frequency reach to the stable value promptly. Finally the the Monte-Carlo simulation results show the effectiveness of the proposed algorithm.

Key words： Signal processing Frequency-Hopping (FH) Frequency estimation Direction Of Arrival (DOA) Particle filtering Auto Regresive Moving Average (ARMA) model

Received: 23 October 2015 Published: 09 August 2016

PACS:

TN911.7

Fund:

The National Natural Science Foundation of China (61401499), The Foundation of Electronic Information System Integration Laboratory in Shaanxi Province (201501A)，The Aviation Science Foundation of China (20112096016)

Corresponding Authors: ZHANG Dongwei E-mail: zdw_dsp616@163.com

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	ZHANG Dongwei
	GUO Ying
	ZHANG Kunfeng
	QI Zisen
	HAN Lifeng
	SHANG Yaobo

Cite this article:

ZHANG Dongwei,GUO Ying,ZHANG Kunfeng等. Online Estimation Algorithm of 2D-DOA and Frequency Tracking for Multiple Frequency-hopping Signals[J]. JEIT, 2016, 38(9): 2377-2384.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT151170 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I9/2377

[1]	ZHAO Lifan, WANG Lu, BI Guoan, et al. Robust frequency- hopping spectrum estimation based on sparse Bayesian method[J]. IEEE Transactions on Wireless Communications, 2015, 14(2): 781-793.
[2]	张东伟, 郭英, 齐子森, 等. 多跳频信号波达方向与极化状态联合估计算法[J]. 电子与信息学报, 2015, 37(7): 1695-1701. doi: 10.11999/JEIT141315.
	ZHANG Dongwei, GUO Ying, QI Zisen, et al. Joint estimation algorithm of direction of arrival and polarization for multiple frequency-hopping signals[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1695-1701. doi: 10.11999/JEIT141315.
[3]	FU Weihong, HEI Yongqiang, and LI Xiaohui. UBSS and blind parameters estimation algorithms for synchronous orthogonal FH signals [J]. Journal of Systems Engineering and Electronics, 2014, 25(6): 911-920. doi: 10.1109/JSEE.2014. 00105.
[4]	赵新明, 金艳, 姬红兵. 稳定分布噪声下基于Merid滤波的跳频信号参数估计[J]. 电子与信息学报, 2014, 36(8): 1878-1883. doi: 10.3724/SP.J.1146.2013.01436.
	ZHAO Xinming, JIN Yan, and JI Hongbing. Parameter estimation of frequency-hopping signals based on Merid filter in a stable noise environment[J]. Journal of Electronics & Information Technology, 2014, 36(8): 1878-1883. doi: 10.3724/ SP.J.1146.2013.01436.
[5]	ANGELOSANTE D, GIANNAKIS G B, and SIDIROPOULOS N D. Sparse parametric models for robust nonstationary signal analysis: Leveraging the power of sparse regression[J]. IEEE Signal Processing Magazine, 2013, 30(6): 64-73.
[6]	SHA Zhichao, HUANG Zhitao, ZHOU Yiyu, et al. Frequency-hopping signals sorting based on underdetermined blind source separation[J]. IET Communications, 2013, 14(7): 1456-1464.
[7]	FU Kuoching and CHEN Yungfang. Blind iterative maximum likelihood-based frequency and transition time estimation for frequency hopping systems[J]. IET Communications, 2013, 7(9): 883-892.
[8]	VALYAKIS A, TSAKONAS E E, SIDIROPOULOS N D, et al. Stochastic modelling and particle filtering algorithms for tracking a frequency-hopped signal[J]. IEEE Transactions on Signal Processing, 2009, 57(8): 3108-3118.
[9]	李世鹏, 赵正予, 黄硕, 等. 一种基于粒子滤波的跳频信号频率跟踪技术[J]. 华中科技大学学报(自然科学版), 2011, 39(9): 33-37. doi: 10.13245/j.hust.2011.09.009.
	LI Shipeng, ZHAO Zhengyu, HUANG Shuo, et al. Frequency tracking technology for frequency-hopping signals using particle filters[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2011, 39(9): 33-37. doi: 10.13245/j.hust.2011.09.009.
[10]	LIU Zhangmeng, HUANG Zhitao, and ZHOU Yiyu. Hopping instants detection and frequency tracking of FH signals with
	single or multiple channels[J]. IET Communications, 2012, 6(1): 84-89.
[11]	SHA Zhichao, LIU Zhangmeng, HUANG Zhitao, et al. Online hop timing detection and frequency estimation of multiple FH signals[J]. ETRI Journal, 2013, 35(5): 748-756.
[12]	王丰华, 沙志超, 刘章孟, 等. 基于贝叶斯稀疏学习的多跳频信号频率跟踪方法[J]. 电子与信息学报, 2013, 35(6): 1395-1399. doi: 10.3724/SP.J.1146.2012.01493.
	WANG Fenghua, SHA Zhichao, LIU Zhangmeng, et al. A frequency tracking method for multiple frequency-hopping signals based on sparse bayesian learning[J]. Journal of Electronics & Information Technology, 2013, 35(6): 1395-1399. doi: 10.3724 /SP.J.1146.2012.01493.
[13]	LAROCQUE J R, REILLY J P, and NG W. Particle filters for tracking an unknown number of sources[J]. IEEE Transactions on Signal Processing, 2002, 50(12): 2926-2937.
[14]	ANDRIEU C and DOUCET A. Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC[J]. IEEE Transactions on Signal Processing, 1999, 47(10): 2667-2676.
[15]	毛维平, 李国林, 谢鑫. 单快拍数据预处理的相干信号波达方向估计[J]. 上海交通大学学报, 2014, 48(10): 1362-1367.
	MAO Weiping, LI Guolin, and XIE Xin. DOA estimation of coherent signals with preprocessed single snapshot[J]. Journal of Shanghai Jiaotong University, 2014, 48(10): 1362-1367.