Human Target Localization Using Doppler Through-Wall Radar Based on Micro-Doppler Frequency Estimation

Ding, Yipeng; Sun, Yuning; Huang, Guo-Wei; Liu, Runjin; Yu, Xiali; Xu, Xuemei

doi:10.1109/jsen.2020.2983104

Cited by 18 publications

(3 citation statements)

References 35 publications

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“…From this map, the resulting velocity of the target is about 1 [m/s], and approaching the Radar position. The elongated shape is due to the micro-Doppler effect [45].…”

Section: A Proposed Methodsmentioning

confidence: 99%

mmDetect: YOLO-Based Processing of mm-Wave Radar Data for Detecting Moving People

Raimondi,

Ciattaglia,

Nocera

et al. 2024

IEEE Sensors J.

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“…From this map, the resulting velocity of the target is about 1 [m/s], and approaching the Radar position. The elongated shape is due to the micro-Doppler effect [45].…”

Section: A Proposed Methodsmentioning

confidence: 99%

mmDetect: YOLO-Based Processing of mm-Wave Radar Data for Detecting Moving People

Raimondi,

Ciattaglia,

Nocera

et al. 2024

IEEE Sensors J.

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“…IEEE Transactions on Aerospace and Electronic Systems. https://doi.org/10.1109/TAES.2023.3312064 [11]- [12]- [13]- [14]- [15]. To distinguish the answers from various targets, it is important to obtain a synthetic feature that preserves the original signal's attribute.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Micro-Doppler Classification Using Superlet-Based Time-Frequency Distribution

Mignone,

Ilioudis,

Clemente

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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Classical time-frequency distributions, as the Short Time Fourier Transform (STFT) or the Continuous Wavelet Transform (CWT), aim to enhance either the resolution in time or frequency, or attempt to strike a balance between the two. In this paper, we demonstrate how a super resolution technique, the Superlet based time frequency distribution, named Superlet Transform (SLT), can boost the performance of existing classification algorithms relying on information extraction from the micro-Doppler signature. SLT is applied to provide a time-frequency distribution with finer resolutions that would boost the performance of micro-Doppler classification approaches based on time-frequency distributions (TFDs). This work shows the effectiveness of the integration of SLT in the processing pipeline with verification on real radar data.

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“…Since Hough transform can map the time-frequency distribution on the parameter space through achieving energy accumulation along the Doppler frequency change rule of the signal, it is usually used to estimate the micro-Doppler parameters from the time-frequency domain spectrum. The Hough transform was combined with STFT, WVD, PWVD, and RSPWVD for parameter estimation in the Hough-STFT algorithm [18], Hough-WVD algorithm [15], Hough-PWVD algorithm [6], and Hough-RSPWVD algorithm [19], respectively. In these algorithms, the correct micro-Doppler parameters can be estimated from parameter candidate sets by finding the location of the maximal Hough integration in these algorithms.…”

Section: Introductionmentioning

confidence: 99%

Efficient Rotational Angular Velocity Estimation of Rotor Target via Modified Short-Time Fractional Fourier Transform

Wang¹,

Zhu²,

Tang³

et al. 2021

Remote Sensing

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As a special micro-motion feature of rotor target, rotational angular velocity can provide a discriminant basis for target classification and recognition. In this paper, the authors focus on an efficient rotational angular velocity estimation method of the rotor target is based on the combination of the time–frequency analysis algorithm and Hough transform. In order to avoid the problems of low time–frequency resolution and cross-term interference in short-time Fourier transform and Wigner–Ville distribution algorithm, a modified short-time fractional Fourier transform (M-STFRFT) is proposed to obtain the time-FRFT domain (FRFD)-frequency spectrum with the highest time–FRFD–frequency resolution. In particular, an orthogonal matching pursuit (OMP)-based algorithm is proposed to reduce the computational complexity when estimating the matched transform order in the proposed M-STFRFT algorithm. Firstly, partial transform order candidates are selected randomly from the complete candidates. Then, a partial entropy vector corresponding to partial transform order candidates is calculated from the FRFT results and utilized to reconstruct the complete entropy vector via the OMP algorithm, and the matched transform order can be estimated by searching minimum entropy. Based on the estimated matched transform order, STFRFT is performed to obtain the time–FRFD–frequency spectrum. Moreover, Hough transform is employed to obtain the energy accumulation spectrum, and the micro-Doppler parameter of rotational angular velocity can be estimated by searching the peak value from the energy accumulation spectrum. Both simulated data and measured data collected by frequency modulated continuous wave radar validate the effectiveness of the proposed algorithm.

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Human Target Localization Using Doppler Through-Wall Radar Based on Micro-Doppler Frequency Estimation

Cited by 18 publications

References 35 publications

mmDetect: YOLO-Based Processing of mm-Wave Radar Data for Detecting Moving People

mmDetect: YOLO-Based Processing of mm-Wave Radar Data for Detecting Moving People

Enhancing Micro-Doppler Classification Using Superlet-Based Time-Frequency Distribution

Efficient Rotational Angular Velocity Estimation of Rotor Target via Modified Short-Time Fractional Fourier Transform

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