Fast and Refined Processing of Radar Maneuvering Target Based on Hierarchical Detection via Sparse Fractional Representation

Chen, Xiaolong; Guan, Jian; Wang, Guoqing; Ding, Hao; Huang, Yong

doi:10.1109/access.2019.2947169

Cited by 17 publications

(9 citation statements)

References 31 publications

(41 reference statements)

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“…Our CAST avian radar system uses the m-D features for the preliminary classification of targets. In addition, using the flight trajectory characteristics of the target becomes a complementary method to distinguish the flying birds from the UAV target, which is based on the assumption that birds have higher maneuverability (Chen et al, 2019a(Chen et al, , 2019b. Some possible motion models of UAV and bird target are established, and then the target classification is realized by estimating the model conversion frequency of the target.…”

Section: Identification and Classification Of Birds And Other Targetsmentioning

confidence: 99%

Review on critical technology development of avian radar system

Chen

Huang

et al. 2022

AEAT

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Purpose This paper aims to review the critical technology development of avian radar system at airports. Design/methodology/approach After the origin of avian radar technology is discussed, the target characteristics of flying birds are analyzed, including the target echo amplitude, flight speed, flight height, trajectory and micro-Doppler. Four typical airport avian radar systems of Merlin, Accipiter, Robin and CAST are introduced. The performance of different modules such as antenna, target detection and tracking, target recognition and classification, analysis of bird information together determines the detection ability of avian radar. The performances and key technologies of the ubiquitous avian radar are summarized and compared with other systems, and their applications, deployment modes, as well as their advantages and disadvantages are introduced and analyzed. Findings The ubiquitous avian radar achieves the long-time integration of target echoes, which greatly improves detection and classification ability of the targets of birds or drones, even under strong background clutter at airport. In addition, based on the big data of bird situation accumulated by avian radar, the rules of bird activity around the airport can be mined to guide the bird avoidance work. Originality/value This paper presented a novel avian radar system based on ubiquitous digital radar technology. The authors’ experience has confirmed that this system can be effective for airport bird strike prevention and management. In the future, the avian radar system will see continued improvement in both software and hardware, as the system is designed to be easily extensible.

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Section: Identification and Classification Of Birds And Other Targetsmentioning

confidence: 99%

Review on critical technology development of avian radar system

Chen

Huang

et al. 2022

AEAT

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“…In other words, the result of the FRFT of the signal is the most concentrated in the matched transform order. As a matter of fact, if the chirp rate of the signal s F (t m ) is µ 0 , the matched transform order is p 0 = 2α 0 /π, where α 0 is the matched transform angle and can be expressed as [26,31]:…”

Section: Time-frequency Analysismentioning

confidence: 99%

“…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]. These algorithms implement FRFT and improve the effectiveness of FRFT through sparse representation.…”

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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“…In recent years, sparse representation has received considerable interest from experts and researchers in many fields, which approximately reconstructs the query sample with a combination of training samples from all classes [41]- [44]. So far, SR has been extensively employed in many theoretical researches and industrial applications, especially in the fields of signal processing [45], [46] and pattern recognition [47], [48].…”

Section: A Sparse Representationmentioning

confidence: 99%

Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

2020

IEEE Access

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It is always a serious challenge to deal with the influence of noise for fault detection of rotating electrical machines under varying operating environments. To extract the most discriminative features for machine fault classification, a novel dimensionality reduction algorithm called joint sparsity and collaboration preserving projections (JSCPP) is proposed in this paper, based on the platform of graph embedding framework. A joint combination of sparse representation (SR) and collaborative representation (CR) is used to construct the intrinsic and penalty graphs. SR emphasizes only on the dictionary atoms highly correlated to the query sample for local optimum while CR takes every atom into consideration for global optimum, thus applying a joint combination would make it benefit from the merits of both for graph construction. After JSCPP seeks the optimal projection directions by minimizing the intra-class compactness and maximizing the inter-class separability, a nearest neighbor classifier is then used. Through two sets of vibration data with white Gaussian noise interfered, the capability of JSCPP is demonstrated by effectively classifying different defect types and severity degrees of faulty bearings. The experimental results have also proved that the classification performance of the proposed method is much superior to the other compared algorithms in both accuracy rates and reliability.

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Fast and Refined Processing of Radar Maneuvering Target Based on Hierarchical Detection via Sparse Fractional Representation

Cited by 17 publications

References 31 publications

Review on critical technology development of avian radar system

Review on critical technology development of avian radar system

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

Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

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