Cyclostationary Phase Analysis on Micro-Doppler Parameters for Radar-Based Small UAVs Detection

Zhao, Yichao; Su, Yi

doi:10.1109/tim.2018.2811256

Cited by 53 publications

(22 citation statements)

References 31 publications

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“…UAVs using radars [6,11,15,[17][18][19][20][21][22]28]. Artificial neural networks were applied on spectrum directly to classify different types of UAVs [28].…”

Section: Machine-learning Techniques Have Been Utilized To Automaticamentioning

confidence: 99%

“…Patel et al applied Alexnet on four time-frequency representations including spectrogram, cepstrogram and CVD for UAV classification [22]. Zhao and Su developed a cyclostationary analysis on the phase term of the radar signal to extract the mDS for UAV detection [18]. Very recently, empirical mode decomposition was employed to extract intrinsic mode functions for UAV classification [19].…”

Section: Machine-learning Techniques Have Been Utilized To Automaticamentioning

confidence: 99%

“…Early techniques using kinematic and radar cross-section characteristics [3] could not reliably differentiate UAVs from birds, as the both kinds are small and slow-moving targets. Many representations of micro-Doppler signature (mDS) have been explored, e.g., spectrogram [4][5][6][7][8][9][10][11], cepstrogram [12], cadence velocity diagram [13][14][15], others [16][17][18][19], and combinations of the aforementioned [20][21][22]. Particularly in [22], a rich source of features including spectrogram, cepstrogram and CVD are utilized.…”

Section: Introductionmentioning

confidence: 99%

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A three-step classification framework to handle complex data distribution for radar UAV detection

Ren

Jiang

2021

Pattern Recognition

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Unmanned aerial vehicles (UAVs) have been used in a wide range of applications and become an increasingly important radar target. To better model radar data and to tackle the curse of dimensionality, a three-step classification framework is proposed for UAV detection. First we propose to utilize the greedy subspace clustering to handle potential outliers and the complex sample distribution of radar data. Parameters of the resulting multi-Gaussian model, especially the covariance matrices, could not be reliably estimated due to insufficient training samples and the high dimensionality. Thus, in the second step, a multi-Gaussian subspace reliability analysis is proposed to handle the unreliable feature dimensions of these covariance matrices. To address the challenges of classifying samples using the complex multi-Gaussian model and to fuse the distances of a sample to different clusters at different dimensionalities, a subspace-fusion scheme is proposed in the third step. The proposed approach is validated on a large benchmark dataset, which significantly outperforms the state-of-the-art approaches.

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“…UAVs using radars [6,11,15,[17][18][19][20][21][22]28]. Artificial neural networks were applied on spectrum directly to classify different types of UAVs [28].…”

Section: Machine-learning Techniques Have Been Utilized To Automaticamentioning

confidence: 99%

Section: Machine-learning Techniques Have Been Utilized To Automaticamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A three-step classification framework to handle complex data distribution for radar UAV detection

Ren

Jiang

2021

Pattern Recognition

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“…In recent years, the number of small UAVs has grown exponentially due to their significant improvement of flight performance, low cost, and easy manipulation. UAVs are widely applied in professional photography, shooting, agricultural applications, and disaster search-and-rescue [1]. They are used for criminal activities such as invasion, reconnaissance, and transport of explosives [2].…”

Section: Introductionmentioning

confidence: 99%

UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm

et al. 2021

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A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time–frequency characteristics and texture features of the UAV’s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.

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“…Therefore, keeping continuous and uninterrupted track of the deployed UAVs should be a high‐priority task. However, commercial UAVs tend to fly at low speeds and altitudes with a small radar cross‐section (RCS) [3]. Moreover, the weakness of their reflected signals render the radar echoes vulnerable to interference by communication, broadcast, clutter etc.…”

Section: Introductionmentioning

confidence: 99%

Robust direction of arrival estimation approach for unmanned aerial vehicles at low signal‐to‐noise ratios

Lü

Yang

Liu

et al. 2019

IET signal process.

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The conventional methods used to estimate the direction of arrival (DOA) of linear frequency modulated (LFM) signals at low signal-to-noise ratios (SNRs), such as the echoes reflected by a small unmanned aerial vehicle (UAV), demonstrate major performance deterioration. In order to eliminate the problem and achieve highly accurate DOA estimation for low-SNR echoes, this paper proposes a novel estimation approach that applies two accumulative methods based on fractional Fourier transform (FrFT). According to the findings of this paper, the algorithm directly accumulates the FrFT result of each echo when it is not possible to determine in advance the speed of the target. When the radial velocity of the small UAV has been estimated ahead of time, the algorithm performs phase compensation to enhance the accumulative effect. Through coherent integration, the algorithm then extracts all the peaks of the target echo waveform, which are then used for the construction of a fractional autocorrelation matrix. Thereafter, multiple signal classification is employed for DOA estimation. Furthermore, with the proposed algorithm, the DOAs of multi-target echoes can be estimated accurately. The effectiveness of the proposed algorithm was verified using Monte-Carlo simulation trials, and the root-mean-square-error of DOA estimation was close to the Cramer-Rao bound.

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Cyclostationary Phase Analysis on Micro-Doppler Parameters for Radar-Based Small UAVs Detection

Cited by 53 publications

References 31 publications

A three-step classification framework to handle complex data distribution for radar UAV detection

A three-step classification framework to handle complex data distribution for radar UAV detection

UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm

Robust direction of arrival estimation approach for unmanned aerial vehicles at low signal‐to‐noise ratios

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