一种多分量调频信号瞬时频率估计方法

doi:10.19665/j.issn1001-2400.2019.01.009

摘要/Abstract

摘要：

针对多分量调频信号的相位结构分析问题,提出了一种基于维特比算法的瞬时频率估计方法。该算法受多目标跟踪中的航迹关联思想启发,将瞬时频率轨迹追踪同多目标航迹跟踪相结合,建立了一种新型隶属度惩罚函数,解决了原维特比算法只适用于单分量信号的问题。此外,还提出了一种新型时频交叉点的处理方法,能够提高多分量信号瞬时频率交叉时的估计精度。仿真表明,相较于现有的同类型算法,该方法能够准确地获取复杂相位结构的多分量信号瞬时频率信息,有效地提升了维特比算法在多分量信号瞬时频率估计上的适用性和稳健性。

关键词: 多分量调频信号, 维特比算法, 瞬时频率估计, 航迹关联

Abstract:

Aiming at the key problem on the phase structure analysis for multicomponent FM signals, an IF(Instantaneous Frequency) estimation method based on the Viterbi algorithm is proposed. Inspired by the idea of track association in multi-target tracking, combining IF trajectory tracking with multi-target tracking, a new membership penalty function is introduced to broaden adaptability and the proposed method performs better than the original VA which is only applicable to single-component signals. Furthermore, a new TF(Time-Frequency) cross-point processing method is proposed to improve the estimation accuracy of multicomponent signals. According to simulation results, the proposed method can obtain accurate IF information on multicomponent signals with a complex phase structure. Compared to the existing algorithms, it is shown that the proposed algorithm based on the VA improves effectively the applicability and robustness for IF estimation of multicomponent signals.

Key words: multicomponent frequency modulation signals, Viterbi algorithm, instantaneous frequency estimation, track association

中图分类号:

TN911.23

苏小凡,肖瑞,朱明哲. 一种多分量调频信号瞬时频率估计方法[J]. 西安电子科技大学学报, 2019, 46(1): 51-56.

SU Xiaofan,XIAO Rui,ZHU Mingzhe. Method for IF estimation of multicomponent FM signals[J]. Journal of Xidian University, 2019, 46(1): 51-56.

图/表 7

图1

图2

图3

图4

图5

图6

图7

参考文献 15

[1]	LU W L, XIE J W, WANG H M , et al. Parameterized Time-frequency Analysis to Separate Multi-radar Signals[J]. Journal of Systems Engineering and Electronics, 2017,28(3):493-502. doi: 10.21629/JSEE.2017.03.09
[2]	AIORDACHIOAIE D. An Analysis System of Sonar Signals Based on Time-frequency Representation [C]//Proceedings of the 2017 9th International Conference on Electronics, Computers and Artificial Intelligence. Piscataway: IEEE, 2017: 1-4.
[3]	CELKA P, BOASHASH B, COLDITZ P . Preprocessing and Time-frequency Analysis of Newborn EEG Seizures[J]. IEEE Engineering in Medicine and Biology Magazine, 2001,20(5):30-39. doi: 10.1109/51.956817 pmid: 11668894
[4]	刘晗, 张建中, 黄忠来 . 基于同步挤压S变换的地震信号时频分析[J]. 石油地球物理勘探, 2017,4(52):689-695. doi: 10.13810/j.cnki.issn.1000-7210.2017.04.007
	LIU Han, ZHANG Jianzhong, HUANG Zhonglai . Time-grequency Analysis of Seismic Data Using Synchro-squeezing S- Transform[J]. Oil Geophysical Prospecting, 2017,4(52):689-695. doi: 10.13810/j.cnki.issn.1000-7210.2017.04.007
[5]	BOASHASH B . Estimating and Interpreting the Instantaneous Frequency of a Signal[J]. Proceedings of the IEEE, 1992,80(4):540-568. doi: 10.1109/5.135378
[6]	BARKAT B, BOASHASH B . Instantaneous Frequency Estimation of Polynomial FM Signals Using the Peak of the PWVD: Statistical Performance in the Presence of Additive Gaussian Noise[J]. IEEE Transactions on Signal Processing, 1999,47(9):2480-2490. doi: 10.1109/78.782191
[7]	DJUROVIC I, STANLOVIC L . An Algorithm for the Wigner Distribution Based Instantaneous Frequency Estimation in a High Noise Environment[J]. Signal Processing, 2004,84(3):631-643. doi: 10.1016/j.sigpro.2003.12.006
[8]	STANKOVIC L, DJUROVIC I, OHSUMI A, et al. Instantaneous Frequency Estimation by Using Wigner Distribution and Viterbi Algorithm [C]//Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing. Piscataway: IEEE, 2003: 121-124.
[9]	DJUROVIC I . Viterbi Algorithm for Chirp-rate and Instantaneous Frequency Estimation[J]. Signal Processing, 2011,91(5):1308-1314. doi: 10.1016/j.sigpro.2010.10.007
[10]	LI P, WANG D C, WANG L . Separation of Micro-Doppler Signals Based on Time Frequency Filter and Viterbi Algorithm[J]. Signal Image and Video Processing, 2013,7(3):593-605. doi: 10.1007/s11760-011-0263-3
[11]	LI P, ZHANG Q H . An Improved Viterbi Algorithm for IF Extraction of Multicomponent Signals[J]. Signal Image and Video Processing, 2018,12(1):171-179. doi: 10.1007/s11760-017-1143-2
[12]	PANTA K, CLARK D E, VO B N . Data Association and Track Management for the Gaussian Mixture Probability Hypojournal Density Filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009,45(3):1003-1016. doi: 10.1109/TAES.2009.5259179
[13]	欧阳成 . 基于随机集理论的被动多传感器多目标跟踪[D]. 西安: 西安电子科技大学, 2012.
[14]	ZHAO H C, SHA Z C, WU J. An Improved Fuzzy Track Association Algorithm Based on Weight Function [C]//Proceedings of the 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference. Piscataway: IEEE, 2017: 1125-1128.
[15]	ZHANG H J, BI G A, YANG W , et al. IF Estimation of FM Signals Based on Time-frequency Image[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015,51(1):326-343. doi: 10.1109/taes.2014.130554