多跳频信号频率跟踪与二维波达方向实时估计算法

张东伟; 郭英; 张坤峰; 齐子森; 韩立峰; 尚耀波

doi:10.11999/JEIT151170

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

2.
(空军工程大学装备发展与运用研究中心西安 710051) ②(空军工程大学信息与导航学院西安 710077)

基金项目:

国家自然科学基金(61401499)，陕西省电子信息系统综合集成重点实验室基金(201501A)，航空科学基金(20112096016)

计量
- 文章访问数: 1189
- HTML全文浏览量: 105
- PDF下载量: 354
- 被引次数: 0
出版历程
- 收稿日期: 2015-10-23
- 修回日期: 2016-07-01
- 刊出日期: 2016-09-19

Online Estimation Algorithm of 2D-DOA and Frequency Tracking for Multiple Frequency-hopping Signals

2.
ZHANG Dongwei①② GUO Ying② ZHANG Kunfeng② QI Zisen② HAN Lifeng① SHANG Yaobo

Funds:

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)

摘要

摘要: 为了实时提取跳频(FH)通信参数以及为通信对抗提供所需信息，该文提出一种多跳频信号频率跟踪和2维波达方向实时估计算法。首先建立跳频信号的L型阵列接收数据模型，并推导证明了自回归滑动平均(ARMA)模型对L型阵列数据的适用性，然后采用粒子滤波思想对阵列流型矩阵和频率进行实时估计。再基于频率估计值建立ARMA模型实时检测跳时刻，并结合流型矩阵估计值实现无需参数配对的2维波达方向(2D-DOA)准确估计。新方法通过设计合理的粒子生成以及权值更新方式，使流型矩阵与频率估计值能够迅速收敛至稳定状态。最后蒙特卡罗仿真结果验证了该算法的有效性。
- 信号处理 /
- 跳频 /
- 频率估计 /
- 波达方向 /
- 粒子滤波 /
- 自回归滑动平均模型
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.
- Signal processing /
- Frequency-Hopping (FH) /
- Frequency estimation /
- Direction Of Arrival (DOA) /
- Particle filtering /
- Auto Regresive Moving Average (ARMA) model

HTML全文

参考文献(21)

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

施引文献

资源附件(0)

访问统计