基于自适应网格的跳频信号参数估计
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摘要
分析了现有跳频信号稀疏重构算法的基不匹配问题,导致离散字典的稀疏表示能力变差,严重影响稀疏重构算法的性能。针对这种情形,提出了基于自适应网格的变分贝叶斯稀疏重构算法。该方法通过对字典不断地加权聚类和缩放处理,实现字典的自我更新,使得参数网格更加精细化。仿真结果表明,该方法具有良好的抗噪性能和交叉项抑制能力,同时缓解了稀疏重构算法的基不匹配情形,时频聚焦性进一步提高,能够在较低信噪比条件下,获取较高时频分辨率的时频矩阵,可以更精确地完成后续跳时刻检测、跳周期及跳频率等参数估计。
The base mismatch problem of existing sparse reconstruction algorithms for frequency hopping signals leads to poor sparse representation ability of discrete dictionaries,which seriously affects the performance of sparse reconstruction algorithms.In view of this situation,a variational Bayesian sparse reconstruction algorithm based on adaptive mesh is proposed.The method realizes self-renewal of the dictionary by continuously weighting clustering and scaling processing on the dictionary,which makes the parameter mesh more refined.The simulation results show that the proposed method has good anti-noise and cross-term suppression ability.At the same time,the base mismatch of the sparse reconstruction algorithm is alleviated,and the time-frequency focusing is further improved.Under the condition of lower signal-to-noise ratio,the time-frequency matrix with higher time-frequency resolution can be obtained,and the time-hopping detection and estimation of parameters such as hopping period and frequency can be accomplished more accurately.
The base mismatch problem of existing sparse reconstruction algorithms for frequency hopping signals leads to poor sparse representation ability of discrete dictionaries,which seriously affects the performance of sparse reconstruction algorithms.In view of this situation,a variational Bayesian sparse reconstruction algorithm based on adaptive mesh is proposed.The method realizes self-renewal of the dictionary by continuously weighting clustering and scaling processing on the dictionary,which makes the parameter mesh more refined.The simulation results show that the proposed method has good anti-noise and cross-term suppression ability.At the same time,the base mismatch of the sparse reconstruction algorithm is alleviated,and the time-frequency focusing is further improved.Under the condition of lower signal-to-noise ratio,the time-frequency matrix with higher time-frequency resolution can be obtained,and the time-hopping detection and estimation of parameters such as hopping period and frequency can be accomplished more accurately.
引文
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[2]ZHENG Y,CHEN X,ZHU R.Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform[J].Modern Physics Letters B,2017,31(19/21):2.
[3]CHEN T C.Joint signal parameter estimation of frequency-hopping communications[J].Iet Communications,2012,6(4):381-389.
[4]LIANG Z J,LV M.A joint rapid parameter estimate method of frequency-hopping signals[C]∥Proc.of the IEEE International Conference on Control Engineering&Communication Technology,2013.
[5]QIAN B,ZHANG X Y,SUN S.A parameters estimation method for FH signal based on SPWVD[J].Advanced Materials Research,2011,912-914(18):1112-1115.
[6]付卫红,王璐,贾坤,等.基于STFT与SPWVD的跳频参数盲估计算法[J].华中科技大学学报(自然科学版),2014(9):59-63.FU W H,WANG L,JIA K,et al.Blind frequency estimation algorithm based on STFT and SPWVD[J].Journal of Huazhong University of Science and Technology:Natural Science,2014(9):59-63.
[7]LIU F,MARCELLIN M W,GOODMAN N A,et al.Compressive sampling for detection of frequency-hopping spread spectrum signals[J].IEEE Trans.on Signal Processing,2016,64(21):5513-5524.
[8]LIU S,ZHANG Y D,TAO S,et al.Structure-aware Bayesian compressive sensing for frequency-hopping spectrum estimation with missing observations[J].IEEE Trans.on Signal Processing,2018,66(8):2153-2166.
[9]ZHANG Y,HONG X,WANG Y,et al.Gridless SPICE applied to parameter estimation of underwater acoustic Frequency Hopping signals[C]∥Proc.of the Ocean Acoustics,2016.
[10]ZHANG C,WANG Y,JING F.Underdetermined blind source separation of synchronous orthogonal frequency hopping signals based on single source points detection[J].Sensors,2017,17(9):2074.
[11]付卫红,张云飞,韦娟.基于滑窗和原子字典的压缩域跳频信号参数估计算法[J].电子与信息学报,2017(11).FU W H,ZHANG Y F,WEI J.Parameter estimation algorithm for frequency-hopping signal in compressed domain based on sliding window and atomic dictionary[J].Journal of Electronics&Information Technology,2017,39(11):2600-2606.
[12]LIU S,ZHANG Y D,SHAN T.Sparsity-based frequencyhopping spectrum estimation with missing samples[C]∥Proc.of the IEEE Radar Conference,2016.
[13]ZHANG Q,LIU Y H,ZHANG X X.Parameter estimation of non-modulated or modulated frequency-hopping signals[C]∥Proc.of the IEEE International Conference on Signal Processing,2016.
[14]WANG Y,YANG X,ZHANG Y,et al.The parameter estimation of frequency-hopping signals via l1-SPICE[C]∥Proc.of the IEEE Ocean Acoustics,2016.
[15]LEI C,PENG H,LIU M,et al.Compressed sensing for frequency hopping communication signals via analog to information converter[C]∥Proc.of the IEEE International Conference on Network Infrastructure&Digital Content,2017.
[16]BID,XIE Y,LAN M,et al.Multifrequency compressed sensing for 2-D near-field synthetic aperture radar image reconstruction[J].IEEE Trans.on Instrumentation&Measurement,2017,66(4):777-791.
[17]ZUO L,PAN J,MA B Y.Parameter estimation of multiple frequency-hopping signals with two sensors[J].Sensors,2018,18(4):1088.
[18]张坤峰,郭英,齐子森,等.基于稀疏贝叶斯重构的多跳频信号参数估计[J].华中科技大学学报(自然科学版),2017,45(1):97-102.ZHANG K F,GUO Y,QI Z S,et al.Multifrequency hopping signal parameter estimation based on sparse bayesian reconstruction[J].Journal of Huazhong University of Science and Technology:Natural Science,2017,45(1):97-102.
[19]ZHANG K F,GUO Y,QI Z S.A parameter estimation algorithm for multiple frequency-hopping signals based on sparse bayesian method[J].Mathematical Problems in Engineering,2017,2017:1-9.
[20]JI Y,YUAN S,WANG S,et al.Frequency-domain sparse Bayesian learning inversion of AVA data for elastic parameters reflectivities[J].Journal of Applied Geophysics,2016,133:1-8.
[21]HILLI A A,NAJAFIZADEH L,PETROPULU A.Weighted sparse Bayesian learning(WSBL)for basis selection in linear underdetermined systems[C]∥Proc.of the IEEE International Workshop on Compressed Sensing Theory&Its Applications to Radar,2016.
[22]NOLAN T,WAND M.Accurate logistic variational message passing:algebraic and numerical details[J].Stat,2017,6.
[23]ZHU L,HUANG Z,LIU Y,et al.The Nonparametric Bayesian dictionary learning based interpolation method for WSNs missing data[J].AEU-International Journal of Electronics and Communications,2017:S1434841117301279.
[24]PANZERA D,BENEDETTI R,POSTIGLIONE P.A Bayesian approach to parameter estimation in the presence of spatial missing data[J].Spatial Economic Analysis,2015:1-18.
[25]FU H,KAM P Y.MAP/ML estimation of the frequency and phase of a single sinusoid in noise[J].IEEE Trans.on Signal Processing,2007,55(3):834-845.