Improved time difference of arrival estimation algorithms for cyclostationary signals in α-stable impulsive noise

Liu, Yang; Zhang, Yinghui; Qiu, Tianshuang; Gao, Jing; Na, Shun

doi:10.1016/j.dsp.2018.02.010

Cited by 26 publications

(5 citation statements)

References 28 publications

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“…Since the conventional cyclic statistics are useless for exploiting cyclostationarity in impulsive noise, it is necessary to use other properties of signals to develop robust coherent DOA estimation algorithms. It has been shown in [30] that FLOS-based cyclic statistics can be applied to suppress the impulsive noise for cyclostationary signals, and a new type of pth-order cyclostationarity has been developed. Considering a cyclostationary signal x(t), the pth-order correlation of x(t) is defined by…”

Section: The Proposed Smoothing Fractional Lower-order Cyclic Doa Metmentioning

confidence: 99%

“…The FLOS-based algorithms can provide accurate DOA estimates for Gaussian and non-Gaussian impulsive noises; however, they suffer from poor performance when interfering signal is present which occupies the same spectral band as the source signal. Recent advances on communications, array processing, and identification have indicated that non-Gaussian impulsive noise can be modeled by α-stable distributions [29,30]. According to the Generalized Central Limit Theorem, the Gaussian distribution is the limiting case of the α-stable distribution (α = 2).…”

Section: Introductionmentioning

confidence: 99%

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Cyclostationarity-based DOA estimation algorithms for coherent signals in impulsive noise environments

Liu

Zhang

et al. 2019

J Wireless Com Network

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Estimating direction of arrival (DOA) is important in a variety of practical applications. Conventional cyclostationarity-based coherent DOA estimation algorithms are not robust to non-Gaussian α-stable impulsive noise. Additionally, fractional lower-order statistics (FLOS)-based algorithms are tolerant to impulsive noise; however, they experience performance degradation for coherent signals and interference. To overcome these drawbacks, two types of fractional lower-order cyclostationarity-based subspace DOA estimation methods are proposed for coherent signals in the presence of interference and α-stable impulsive noise. The new proposed algorithms exploit the fractional lower-order cyclostationarity properties of the signals and are immune to the impulsive noise and interference. Moreover, they can provide more accurate DOA estimates of coherent signals than conventional cyclostationarity-based and FLOS-based methods. The simulation results illustrate the robustness and effectiveness of the proposed methods for coherent signals based on a comparison with traditional methods. The new algorithms can be used in the presence of a wide range of interference, Gaussian noise, and α-stable distribution impulsive noise environments.

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Section: The Proposed Smoothing Fractional Lower-order Cyclic Doa Metmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cyclostationarity-based DOA estimation algorithms for coherent signals in impulsive noise environments

Liu

Zhang

et al. 2019

J Wireless Com Network

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“…-stable distribution can describe some random signals and noises with strong spike pulse characteristics and thick trailing characteristics, such as medical signals, communications signals, radar signals 6,7 . The actual application also indicates that -stable distribution is better than the Gaussian distribution for the probability density function fitting of non-Gaussian signals 8,9 .…”

Section: Introductionmentioning

confidence: 96%

Analysis and prediction of supermarket energy consumption time series with significant fractional order characteristics

Wang,

Liu,

et al. 2024

Preprint

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The actual industrial processes are always accompanies by many non-Gaussian behaviors due to the systems complexity. These behaviors are fractional order characteristics, which are very difficult to analyze by traditional analysis methods. This paper presents a detail fractional order theory analyses based on the fractional order characteristics present in industrial process. Initially, the -stable distribution is employed to fit the probability density distribution of the data and the auto correlation function is applied to find the long range dependence characteristic hidden in the process. Next, a re-scaled range method and multifractal detrended fluctuation analysis method is applied to analyze the fractional order features of the process in detail. Then, a fractional auto-regressive integrated moving average model (FARIMA) is proposed to predict accurately of the time series based on the fractional order characteristic of the system. Experimental results show that the superiority for prediction model with fractional order thinking.

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“…In the literature, multiple approaches have been proposed to model impulsive noise, such as α stable distribution [21], Gaussian mixture model [22] and generalized Gaussian model [23], with approach to delay and Doppler estimation. To overcome the problem caused by impulsive noise, fractional lower order statistics-based algorithms were used in [24][25][26][27][28]. However, the performance of these algorithms degrades when the impulsive nature of the noise increases [29].…”

Section: Introductionmentioning

confidence: 99%

Robust Weighted l1,2 Norm Filtering in Passive Radar Systems

Satar

Soysal

Jiang

et al. 2020

Sensors

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Conventional methods such as matched filtering, fractional lower order statistics cross ambiguity function, and recent methods such as compressed sensing and track-before-detect are used for target detection by passive radars. Target detection using these algorithms usually assumes that the background noise is Gaussian. However, non-Gaussian impulsive noise is inherent in real world radar problems. In this paper, a new optimization based algorithm that uses weighted l 1 and l 2 norms is proposed as an alternative to the existing algorithms whose performance degrades in the presence of impulsive noise. To determine the weights of these norms, the parameter that quantifies the impulsiveness level of the noise is estimated. In the proposed algorithm, the aim is to increase the target detection performance of a universal mobile telecommunication system (UMTS) based passive radars by facilitating higher resolution with better suppression of the sidelobes in both range and Doppler. The results obtained from both simulated data with α stable distribution, and real data recorded by a UMTS based passive radar platform are presented to demonstrate the superiority of the proposed algorithm. The results show that the proposed algorithm provides more robust and accurate detection performance for noise models with different impulsiveness levels compared to the conventional methods.

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Improved time difference of arrival estimation algorithms for cyclostationary signals in α-stable impulsive noise

Cited by 26 publications

References 28 publications

Cyclostationarity-based DOA estimation algorithms for coherent signals in impulsive noise environments

Cyclostationarity-based DOA estimation algorithms for coherent signals in impulsive noise environments

Analysis and prediction of supermarket energy consumption time series with significant fractional order characteristics

Robust Weighted l1,2 Norm Filtering in Passive Radar Systems

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