A novel joint parameter estimation method based on fractional ambiguity function in bistatic multiple-input multiple-output radar system

Qiu, Tianshuang; Li, Li

doi:10.1016/j.compeleceng.2013.03.007

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…However, when in the case of SαS distribution noise environment for α < 2, as the second-order correlation function is not bounded, it leads that AF cannot well reflect the signal energy distribution in the ambiguity domain. For this reason, the fractional low order ambiguity function (FLOAF) of nonstationary signal s(t) is defined as [34,35],…”

Section: Craf and Flocrafmentioning

confidence: 99%

“…Firstly, the ambiguity function and the fractional lower order statistics are combined to propose the fractional lower order ambiguity function (FLOAF) for joint estimation of time delay and Doppler shift [34]. The FLOAF was also used for solving the problem of MIMO radar localization under impulsive noise in [35]. Several time-frequency representations based on FLOC were defined and used for machine-bearing fault diagnosis under an impulsive noise environment in [36].…”

Section: Introductionmentioning

confidence: 99%

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Kernel Function-Based Ambiguity Function and Its Application on DOA Estimation in Impulsive Noise

Dou

2022

Sensors

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To solve the problem that the traditional ambiguity function cannot well reflect the time-frequency distribution characteristics of linear frequency modulated (LFM) signals due to the presence of impulsive noise, two robust ambiguity functions: correntropy-based ambiguity function (CRAF) and fractional lower order correntropy-based ambiguity function (FLOCRAF) are defined based on the feature that correntropy kernel function can effectively suppress impulsive noise. Then these two robust ambiguity functions are used to estimate the direction of arrival (DOA) of narrowband LFM signal under an impulsive noise environment. Instead of the covariance matrix used in the ESPRIT algorithm by the spatial CRAF matrix and FLOCRAF matrix, the CRAF-ESPRIT and FLOCRAF-ESPRIT algorithms are proposed. Computer simulation results show that compared with the algorithms only using ambiguity function and the algorithms only using the correntropy kernel function-based correlation, the proposed algorithms using ambiguity function based on correntropy kernel function have good performance in terms of probability of resolution and estimation accuracy under various circumstances. Especially, the performance of the FLOCRAF-ESPRIT algorithm is better than the CRAF-ESPRIT algorithm in the environment of low generalized signal-to-noise ratio and strong impulsive noise.

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Section: Craf and Flocrafmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Kernel Function-Based Ambiguity Function and Its Application on DOA Estimation in Impulsive Noise

Dou

2022

Sensors

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“…Onur Celik et al [36] proposed a generalized formulation based on AF, solving the problem of multiple-input multiple-output radar beampattern design successfully. Aiming at the uncertainty associated with random vibration signals, the fractional Fourier ambiguity function (FFAF) [37] was introduced, and the correlation function [38] was derived. Based on the cross-correlation theory, the relationship between the correlation function and TV physical parameters can be determined.…”

Section: Introductionmentioning

confidence: 99%

Kernel Regression Residual Decomposition-Based Polynomial Frequency Modulation Integral Algorithm to Identify Physical Parameters of Time-Varying Systems under Random Excitation

Liu

Shi

2023

Applied Sciences

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The physical parameters (stiffness, damping) of time-varying (TV) systems under random excitation provide valuable information for their working condition but they are often overwhelmed by noise interference. To overcome this problem, this paper presents a novel multi-level kernel regression residual decomposition method, which can not only effectively separate each modal component from the raw vibration acceleration signal, but also eliminate noise interference. Additionally, the multiple degree-of-freedom (DOF) parameter identification problem is transformed into a single DOF parameter identification problem. Combined with the derived polynomial frequency modulation integral algorithm and the cross-correlation theory based on the fractional Fourier ambiguity function, a physical parameter identification method is proposed. The method provides a new idea in modeling TV systems and identifying physical parameters under random excitation. To demonstrate the effectiveness of the proposed method, numerical simulations are conducted with three different cases of variation (variation, quadratic variation, and periodic variation) in time. Moreover, its robustness is evaluated by adding different signal-to-noise ratio levels of noise (20 dB, 50 dB, 100 dB) to the input vibration acceleration signal. The analysis results confirm the performance of the proposed method for the parameter identification of TV systems under random excitation.

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Generalized Fractional Ambiguity Function and Its Applications

Sahay

Rasheed

Kulkarni

et al. 2020

Circuits Syst Signal Process

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A novel joint parameter estimation method based on fractional ambiguity function in bistatic multiple-input multiple-output radar system

Cited by 6 publications

References 17 publications

Kernel Function-Based Ambiguity Function and Its Application on DOA Estimation in Impulsive Noise

Kernel Function-Based Ambiguity Function and Its Application on DOA Estimation in Impulsive Noise

Kernel Regression Residual Decomposition-Based Polynomial Frequency Modulation Integral Algorithm to Identify Physical Parameters of Time-Varying Systems under Random Excitation

Generalized Fractional Ambiguity Function and Its Applications

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