Estimating Multiple Frequency-Hopping Signal Parameters via Sparse Linear Regression

Angelosante, Daniele; Giannakis, Georgios B.; Sidiropoulos, Nicholas D.

doi:10.1109/tsp.2010.2052614

Cited by 78 publications

(79 citation statements)

References 37 publications

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“…Methods based on time-frequency analysis are attractive due to its simplicity and fast computation, but their performance degrades in low signal-to-noise ratio (SNR) scenarios [8,9]. By contrast, sparse reconstruction based methods achieve good timefrequency spectrum and accurate parameter estimation in low SNR conditions [10][11][12]. However, all these aforementioned methods suffer from degrading performance and mismatching of signals and parameters with multiple FH signals.…”

Section: Introductionmentioning

confidence: 99%

A Parameter Estimation Algorithm for Multiple Frequency‐Hopping Signals Based on Sparse Bayesian Method

Zhang¹,

Guo²,

Qi³

2017

Mathematical Problems in Engineering

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Parameter estimation and network sorting for noncooperative wideband frequency-hopping (FH) signals have been essential and challenging tasks, especially in the case with little or even no prior information at all. In this paper, we propose a nearly blind estimation approach to estimate signal parameters based on sparse Bayesian reconstruction. Taking the sparsity in the spatial frequency domain of multiple FH signals into account, we propose a sparse Bayesian algorithm to estimate the spatial frequency parameters. As a result, the frequency and direction of arrival (DOA) parameters can be obtained. In order to improve the accuracy of the estimation parameters, we employ morphological filter methods to further clean the data poisoned by the noise. Moreover, our method is applicable to the wideband signal models with little prior information. We also conduct extensive numerical simulations to verify the performance of our method. Notably, the proposed method works well even in low signal-to-noise ratio (SNR) environment.

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Section: Introductionmentioning

confidence: 99%

A Parameter Estimation Algorithm for Multiple Frequency‐Hopping Signals Based on Sparse Bayesian Method

Zhang¹,

Guo²,

Qi³

2017

Mathematical Problems in Engineering

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“…To effectively deal with the heavy burden on A/D hardware, multiple FH signal estimation is considered in a sparse linear regression (SLR) framework [11], [12], where a fused-lasso alike formulation is employed. In this framework, two penalty terms are defined to encourage sparsity [13] and smoothness in the TF domain, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…where the first term is for data fitting, the second and third terms are for encouraging sparsity in frequency and differential-time domain, respectively [11]. Since this approach is based on the compressive sensing concept, better estimation accuracy can be achieved even with sub-Nyquist sampling ratio.…”

Section: Introductionmentioning

confidence: 99%

Blind Frequency Hopping Spectrum Estimation: A Bayesian Approach

Zhao

Wang

et al. 2014

2014 IEEE Fourth International Conference on Big Data and Cloud Computing

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Frequency hopping signals have been widely employed in wireless networks due to its robustness in anti-jamming and interference. In the scenario of coexistent networks, however, each user inevitably receives multiple unknown frequency hopping signals from different networks. In wireless networks with energy constraint, conventional re-transmission strategy is not affordable if collision happens. This paper considers the blind frequency hopping signal estimation problem in energy-constraint wireless senor networks, where re-transmission can be avoided due to the capability of robust spectrum estimation. We develop a novel high-resolution time-frequency representation by exploiting sparsity to allow signal sampled with sub-Nyquist ratio and achieve robust estimation performance. In our work, this problem is formulated in a probabilistic framework to induce sparsity statistically. Apart from sparsity, the piecewise smoothness in time-frequency domain is further leveraged with a clustering alike procedure by exerting a dependent Dirichlet process prior over the variance parametric space in an integrated manner. Results of numerical experiments show that the proposed algorithm can achieve superior performance particularly in sub-Nyquist sampling and low signal-to-noise ratio (SNR) scenarios compared with other recently reported ones.

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Section: Introductionmentioning

confidence: 99%

“…Ko and others focused on the estimation of the hop instant with the maximum likelihood method for network synchronization [7]. Angelosante and others focused on the estimation of the hop instant with the sparse linear regression method [8] and obtained a more satisfactory performance than when using the time-frequency distribution method.Although the above-mentioned methods are quite different in principle, they can only make use of the diversity of FH signal hop times to sort FH networks. So, these methods can only be used to sort asynchronous networks whose hop times have different rules.…”

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confidence: 99%

Online Hop Timing Detection and Frequency Estimation of Multiple FH Signals

Sha¹

2013

ETRI J

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This paper addresses the problem of online hop timing detection and frequency estimation of multiple frequencyhopping (FH) signals with antenna arrays. The problem is deemed as a dynamic one, as no information about the hop timing, pattern, or rate is known in advance, and the hop rate may change during the observation time. The technique of particle filtering is introduced to solve this dynamic problem, and real-time frequency and direction of arrival estimates of the FH signals can be obtained directly, while the hop timing is detected online according to the temporal autoregressive moving average process. The problem of network sorting is also addressed in this paper. Numerical examples are carried out to show the performance of the proposed method.Keywords: Array signal processing, frequency hopping, FH, frequency estimation, hop timing detection, network sorting. Manuscript received Nov. 16, 2012; revised Jan. 11, 2013; accepted Feb. 2, 2013. This research was supported by the National Natural Science Foundation of China (No. 61072120), and the Program for New Century Excellent Talents in University of China (NCET).Zhi-Chao Sha (phone: +86 073184573489, shazhichao_163@163.com), Zheng-Meng Liu (zm_liur@sohu.com), Zhi-Tao Huang (taldcn@yahoo.com..cn), and Yi-Yu Zhou (zhouyiyu@shou.com) are with the College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, P.R. China.http://dx.doi.org/10.4218/etrij.13.0112.0787 I. IntroductionFrequency hopping (FH) is one of the prevailing spread spectrum technologies in communications owing to its low probability of detection and interception, and vast efforts have been made to study the parameter estimation methods of such signals. Single-antenna-based methods and multiple-antennabased methods exist that can address this problem.The single-antenna-based methods exploit the temporal properties of the FH signals to estimate their parameters, such as hop rate and frequency. Chung and Polydoros proposed the methods known as multiple-hop observation autocorrelation and single-hop observation autocorrelation to estimate the hop rate and hop timing [1], [2]. The methods in [2] were further developed by Janani and others in [3] to jointly estimate the hop rate and signal power. Barbarossa and Scaglione then introduced the pseudo Wigner-Ville distribution in [4] to estimate the hop period, hop instant, and frequency. The technique of matching pursuit [5] was brought in for blind parameter estimation of FH signals by Fan and others in [6]. Ko and others focused on the estimation of the hop instant with the maximum likelihood method for network synchronization [7]. Angelosante and others focused on the estimation of the hop instant with the sparse linear regression method [8] and obtained a more satisfactory performance than when using the time-frequency distribution method.Although the above-mentioned methods are quite different in principle, they can only make use of the diversity of FH signal hop times to sort FH networks. So, these ...

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Estimating Multiple Frequency-Hopping Signal Parameters via Sparse Linear Regression

Cited by 78 publications

References 37 publications

A Parameter Estimation Algorithm for Multiple Frequency‐Hopping Signals Based on Sparse Bayesian Method

A Parameter Estimation Algorithm for Multiple Frequency‐Hopping Signals Based on Sparse Bayesian Method

Blind Frequency Hopping Spectrum Estimation: A Bayesian Approach

Online Hop Timing Detection and Frequency Estimation of Multiple FH Signals

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