Micro-Doppler separation from Time Frequency Distribution based on direction pattern

Chen, Zhiren; Gu, Hong; Su, Weimin; Wang, Zhao

doi:10.1109/icosp.2014.7015368

Cited by 4 publications

(10 citation statements)

References 7 publications

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“…σ in adjacent periods is a constant and the value is 1.0003 , which proves that (13) is right. The scattering coefficient of the scatter2 is 0, and the scattering coefficient of the scatter3 remains unchanged, which is consistent with (9) and (10). Fig.…”

Section: ( )= ( )supporting

confidence: 89%

“…Set the RMSE of the extracted micro-Doppler as the indicator. The results of the proposed method compared to [10] and [15] are shown in Fig. 8.…”

Section: Figure6 Estimation Of the Precession Periodmentioning

confidence: 99%

“…As for the problem of extracting the micro-Doppler of scattering points, many scholars have carried out researches in this area and obtained significant results. Chen used maxima extraction algorithm to extract the micro-Doppler of single component signal from the time-frequency graph [10]. Zhou applied the Hough transform to separate the micro-motion signal and estimate the micro-Doppler parameters [11].…”

Section: Introductionmentioning

confidence: 99%

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A Method for the Micro-Motion Signal Separation and Micro-Doppler Extraction for the Space Precession Target

Feng

Sisan

2020

IEEE Access

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Aiming at the problem of micro-motion signal separation and micro-Doppler extraction of the precession target, a new method based on singular value decomposition (SVD) and joint approximate diagonalization of eigen-matrices (JADE) is proposed in this paper. Firstly, the micro-motion model of space precession target is established, and the micro-Doppler and scattering characteristics are analyzed to establish the echo signal model of the target. Secondly, through simplifying the signal model of scattering point and building the signal matrix of different signal lengths, the singular value ratio sequence is constructed by the method of SVD to estimate the precession period of the target. Thirdly, the singular vectors of different observation periods are extracted, the observation matrix is constructed, and then the JADE algorithm is adopted to separate the micro-motion signal of each scattering point. Finally, the clustering analysis is used to denoise the time-frequency graph and the centroid calculation is employed to extract the micro-Doppler of each scattering point. Simulation results show that this method of good robustness can effectively separate and extract the micro-Doppler of the scattering points. INDEX TERMS Micro-motion, singular value decomposition, joint approximate diagonalization of eigenmatrices, micro-Doppler extraction. XUGUANG XU was born in Xian, Shaanxi, China, in 1994. He received the B.S. degree in radar engineering in 2016 and received the M.S. degree in electronic science and engineering from Air Force Engineering University, Xi'an, China, in 2018, respectively, where he is currently pursuing the Ph.D. degree. His research interests include micro-motion signal processing and target recognition.

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Section: ( )= ( )supporting

confidence: 89%

“…Set the RMSE of the extracted micro-Doppler as the indicator. The results of the proposed method compared to [10] and [15] are shown in Fig. 8.…”

Section: Figure6 Estimation Of the Precession Periodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Method for the Micro-Motion Signal Separation and Micro-Doppler Extraction for the Space Precession Target

Feng

Sisan

2020

IEEE Access

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“…Recently, the instantaneous frequency (IF) estimation which is based on the concept of S max was used with the aid of other tools and theorem for signal analysis such as direction pattern [6] and Wiener-Khinchine and frequency law [8].…”

Section: Short Time Fourier Transformmentioning

confidence: 99%

“…The time-frequency (T-F) analysis has been identified as a key signal processing tool for inter-pulse analysis, and in this case, the classical and linear but still very much in use short time Fourier transform (STFT) is utilized. Recently in the field of radar signal processing, the STFT has been used in conjunction with fractional Fourier transform (FrFT) for micro-Doppler (m-D) signal removal [5], instantaneous frequency (IF) and direction pattern algorithm for m-D signal estimation [6], independent sub-space analysis (ISA) for narrow-band interference (NBI) mitigation [7], IF and autocorrelation function for classification of airborne radar signal types [8] to mention a few. This paper provides an alternative algorithm based on STFT and its resulting peak to accurately determine the pulse width (PW) and pulse repetition period (PRP) in the presence of additive white Gaussian noise (AWGN), while also presenting the effect of the window functions (Hamming, Hanning, Bartlett and Blackman) on the analysis.…”

Section: Introductionmentioning

confidence: 99%

Selection of window for inter-pulse analysis of simple pulsed radar signal using the short time Fourier transform

Ahmad

Daniyan

Gabriel

2015

IJET

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The electronic intelligence (ELINT) system is used by the military to detect, extract information and classify incoming radar signals. This work utilizes short time Fourier transform (STFT) -time frequency distribution (TFD) for inter-pulse analysis of the radar signal in order to estimate basic radar signal time parameters (pulse width and pulse repetition period). Four well-known windows functions of different and unique characteristics were used for the localization of STFT to determine their various effects on the analysis. The window functions are Hamming, Hanning, Bartlett and Blackman window functions. Monte Carlo simulation is carried out to determine the performance of the signal analysis in presence of additive white Gaussian noise (AWGN). Results show that the lower the transition of main lobe width and higher the peak side lobe, the better the performance of the window function irrespective of time parameter being estimated. This is because 100 percent probability of correct estimation is achieved at signal to noise ratio of about -2dB for Bartlett, 4dB for both Hamming and Hanning, and 9dB for Blackman.

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Recent developments in the use of time-frequency analysis for radar-based applications

Ahmad

Muhammad

2015

Africon 2015

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Micro-Doppler separation from Time Frequency Distribution based on direction pattern

Cited by 4 publications

References 7 publications

A Method for the Micro-Motion Signal Separation and Micro-Doppler Extraction for the Space Precession Target

A Method for the Micro-Motion Signal Separation and Micro-Doppler Extraction for the Space Precession Target

Selection of window for inter-pulse analysis of simple pulsed radar signal using the short time Fourier transform

Recent developments in the use of time-frequency analysis for radar-based applications

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