Sparse long‐time coherent integration‐based detection method for radar low‐observable manoeuvring target

Chen, Xiaolong; Guan, Jian; Chen, Weishi; Zhang, Lin; Yu, Xiaohan

doi:10.1049/iet-rsn.2019.0313

Cited by 23 publications

(18 citation statements)

References 31 publications

(41 reference statements)

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“…But with the rapid development of stealth technology, maneuvering targets are becoming difficult to detect, making radar detection very problematic [1]- [6]. Fortunately, long-term coherent integration is an effective method to enhance the capability of moving target detection since it can provide the highest signal-to-noise ratio (SNR) gain [7]- [10]. However, with the increasing of integration time, the complex motions of maneuvering targets, e.g., high velocity and acceleration, will generate range migration (RM) [11]- [13] and Doppler frequency migration (DFM) [14], [15] both of which seriously degrade the performance.…”

Section: Introductionmentioning

confidence: 99%

Long-Time Coherent Integration for Non-Radial Moving Target Based on Radon Fourier Transform with Modified Variant Angle

Yao

Zhang

Sun

et al. 2022

IEICE Trans. Commun.

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Section: Introductionmentioning

confidence: 99%

Long-Time Coherent Integration for Non-Radial Moving Target Based on Radon Fourier Transform with Modified Variant Angle

Yao

Zhang

Sun

et al. 2022

IEICE Trans. Commun.

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“…Because of the best energy concentration in the FRFT domain (FRFD) with a certain order in terms of the chirp signal, several implementations and fast computational algorithms of FRFT have been derived in recent years; also, they are widely applied in radar signal processing. Inspired by sparse Fourier transform (SFT) [23] and Pei's discrete FRFT [24], a novel sparse discrete FRFT (SDFRFT) was proposed by Liu et al to reduce the computational complexity when dealing with large data sets, which are sparsely represented in the FRFD [25]. Chen et al proposed adaptive sparse fractional Fourier transform algorithm and used it in fast and refined processing of radar maneuvering target [26].…”

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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“…However, it increased the complexity of hardware mixing, and greatly reduced the detection distance of range. Chen et al [18]. proposed a long-time coherent integration (LTCI) method for radar maneuvering targets, especially for low-observable unmanned aerial vehicle targets.…”

Section: Introductionmentioning

confidence: 99%

Detection and Speed Estimation of Moving Target Based on Phase Compensation and Coherent Accumulation Using Fractional Fourier Transform

Yang

Liu

et al. 2020

Sensors

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As unmanned aerial vehicles and other small, low-flying, and low-speed aircrafts are being extensively used, studies on their detection are being extensively conducted in radar application research. However, weak echoes, low Doppler frequencies, and target echoes mixed with ground clutter can considerably degrade the detection performance. Therefore, specific methods for the detection of such targets should be devised. We propose herein a phase compensation and coherent accumulation algorithm based on the fractional Fourier transform (FRFT) for detection and speed estimation of this type of target. First, the energy of the target echo is converged using the FRFT. Next, the phase between the peaks of the target echo is analyzed. Phase compensation and coherent accumulation determined from the expected target speed in the fractional domain eliminate ground clutter and further improve the signal-to-interference-plus-noise ratio. Finally, constant false alarm rate detection is used to identify the target, for which radial speed can be estimated directly according to the peak coordinates. The validity of the algorithm is verified via data simulation and application to real data.

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Sparse long‐time coherent integration‐based detection method for radar low‐observable manoeuvring target

Cited by 23 publications

References 31 publications

Long-Time Coherent Integration for Non-Radial Moving Target Based on Radon Fourier Transform with Modified Variant Angle

Long-Time Coherent Integration for Non-Radial Moving Target Based on Radon Fourier Transform with Modified Variant Angle

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

Detection and Speed Estimation of Moving Target Based on Phase Compensation and Coherent Accumulation Using Fractional Fourier Transform

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