基于压缩感知的跳频信号参数盲估计算法

doi:10.11959/j.issn.1000-436x.2017288

Abstract

Abstract:

A blind parameter estimation algorithm for frequency-hopping signals based on compressed sensing was proposed，in order to solve the problem that the existing parameter estimation algorithms did not take into account the sparse structural characteristics of the signals in frequency domain.Firstly，the maximum cosine method was used to process the segmented compressed sampling signals，and the hopping frequency was estimated.Then，the atom matching algorithm was used to process the signal with the hopping point，and the frequency hopping instance time was estimated accurately.Then the hopping speed and hopping time were estimated.The experimental results show that the algorithm can significantly reduce the sampling data and computational complexity，while improving estimation accuracy.

Key words: frequency-hopping signal, compressive sampling, parameter estimation, atomic matching

CLC Number:

TN914.41

Yun-hong FU,Yun-fei ZHANG,Juan WEI,Nai-an LIU. Blind parameter estimation algorithm for frequency-hopping signals based on compressed sensing[J]. Journal on Communications, 2017, 38(12): 48-56.

Figures/Tables 13

算法计算量及对比	计算复杂度表达式	k＝2	k＝8
分段法(η₁)	$\frac{L N^{2}}{k}$	1.573×10⁷	3.932×10⁶
本文算法(η₂ )	$\frac{(L + 8 B) N^{2}}{k}$ +	4.089×10⁷	1.022×10⁷
原子分解法(η₃)	$\frac{2 L N}{k} \dim (Ψ^{'})$	1.536×10¹⁰	3.840×10⁹
$\frac{η_{1}}{η_{2}}$	$\frac{L}{L + 8 B}$	0.385	0.385
$\frac{η_{3}}{η_{2}}$	$\frac{(L + 8 B) N}{2 L \cdot \dim (Ψ^{'})}$	375.642	375.734

References 13

[1]	LAL P M C , PALSULE V S , RAVI K V . Applications of frequency hopping spread spectrum techniques:an overview[J]. LETE Technical Review, 2015,3(5): 210-220.
[2]	YU S , YOU X , OU W,et,al . STFT-like time frequency representation of nonstationary signal with arbitrary sampling schemes[J]. Neurocomputing, 2016,204(5): 211-221.
[3]	PACHORI R B , NISHAD A . Cross-terms reduction in the Wigner-Ville distribution using tunable-Q wavelet transform[J]. Signal Processing, 2016,120(2016): 288-304.
[4]	赵俊, 张朝阳, 赖利峰 . 一种基于时频分析的跳频信号参数盲估计方法[J]. 电路与系统学报, 2003,8(3): 46-50.
	ZHAO J , ZHANG C Y , LAI L F . Blind parameter estimation of frequency-hopping signals based on time-frequency analysis[J]. Journal of Circuits and Systems, 2003,8(3): 46-50.
[5]	HUANG A M , GUAN G , MEHBODNIYA A ,et al. A block orthogonal matching pursuit algorithm based on sensing dictionary[J]. International Journal of the Physical Sciences, 2011,6(5): 992-999.
[6]	范海宁, 郭英, 艾宇 . 基于原子分解的跳频信号盲检测和参数盲估计算法[J]. 信号处理, 2010,26(5): 695-702.
	FAN H N , GUO Y , AI Y . Blind detection and parameter estimation algorithm based on atomic decomposition[J]. Signal Processing, 2010,26(5): 695-702.
[7]	郭建涛, 王宏远, 余本海 . 基于粒子群算法的跳频信号参数估计[J]. 计算机应用研究, 2010,27(2): 512-514.
	GUO J T , WANG H Y , YU B H . Parameter estimation of frequency hopping signal based on particle swarm optimition[J]. Application Research of Computer, 2010,27(2): 512-514.
[8]	BOUGHER B . Introduction to compressed sensing[J]. Leading Edge, 2015,34(10): 1256-1257.
[9]	HU C , JIN Y K , NA S Y . Compressive frequency hopping signal detection using spectral kurtosis and residual signals[J]. Wireless Personal Communications, 2015,94(1): 53-67.
[10]	LIU F , MICHAEL W . Compressive sampling for detection of frequency-hopping spread spectrum signals[J]. IEEE Transactions on Signal Processing, 2016,64(21): 5513-5524.
[11]	沈长林 . 基于压缩感知的跳频信号参数盲估计[D]. 西安:西安电子科技大学, 2012.
	SHEN C L . Blind parameter estimation of frequency-hopping signal based on compressed sensing[D]. Xi’an:Xidian University, 2012.
[12]	任旭, 朱卫纲 . 基于压缩感知的跳频信号参数估计[J]. 计算机工程与设计, 2016,37(4): 867-871.
	REN X , ZHU W G . Parameter estimation of frequency hopping signals based on compressed sensing[J]. Computer Engineering and Design, 2016,37(4): 867-871.
[13]	张春磊, 李立春, 王大鸣 . 压缩域宽带跳频信号跳变时刻估计算[J]. 太赫兹科学与电子信息学报, 2015,13(1): 122-128.
	ZHANG C L , LI L C , WANG D M . A hopping transition time estimation algorithm for wide-band frequency-hopping signal in compressed domain[J]. Journal of Terahertz Science and Electronic Information Technology, 2015,13(1): 122-128.

Metrics

Recommended 0

No Suggested Reading articles found!

Blind parameter estimation algorithm for frequency-hopping signals based on compressed sensing

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 13

Related Articles 10

Metrics

Recommended 0

[1]	Guojun LI, Cuiling XIANG, Changrong YE, Zunli WANG. Fast link-establishment method of integrated of communication and detection based on short-wave digital channelization [J]. Journal on Communications, 2023, 44(1): 89-102.
[2]	Haifei SI,Xingliu HU,Zhen SHI,Shiyong LI. Non-cooperative signal modulation recognition algorithm based on joint feature parameter extraction [J]. Journal on Communications, 2020, 41(7): 172-185.
[3]	Li LI,Tian-shuang QIU,Ming-yan HE. Novel method based on fractional lower-order correntropy-analogous statistics for parameter jointly estimation in bistatic MIMO radar [J]. Journal on Communications, 2016, 37(12): 42-49.
[4]	. Novel method for joint parameter estimation based on FPSD in wideband bistatic MIMO radar system [J]. Journal on Communications, 2014, 35(6): 25-199.
[5]	Li LI,Tian-shuang QIU. Novel method for joint parameter estimation based on FPSD in wideband bistatic MIMO radar system [J]. Journal on Communications, 2014, 35(6): 192-199.
[6]	Zhi-chao SHA,Zhi-tao HUANG,Yi-yu ZHOU,Jun-hua WANG. Time-frequency analysis of freqency-hopping signals based on sparse recovery [J]. Journal on Communications, 2013, 34(5): 107-112.
[7]	. Time-frequency analysis of freqency-hopping signals based on sparse recovery [J]. Journal on Communications, 2013, 34(5): 12-112.
[8]	Fan-zi ZENG,Jie LIU,Ren-fa LI,Qing-guang ZENG. Distributed cooperative spectrum sensing based on consensus optimization [J]. Journal on Communications, 2011, 32(9): 147-152.
[9]	Ming-qian LIU,Bing-bing LI,Ning-jie TANG,Zhao LI. Blind SNR estimation for OFDM signals in cognitive radio [J]. Journal on Communications, 2011, 32(11): 78-84.
[10]	Dai-ping JIA,Hong-da FAN. Approach for extending sequence memory based on fractionally differenced model [J]. Journal on Communications, 2006, 27(9): 66-70.