micro-Doppler Feature Extraction Research Articles

Extraction of Micro-Doppler Features from Unmanned Aerial Vehicles Based on Electromagnetic Simulation

Extraction of Micro-Doppler Features from Unmanned Aerial Vehicles Based on Electromagnetic Simulation

Extracting Micro-Doppler Features from Multi-Rotor Unmanned Aerial Vehicles Using Time-Frequency Rotation Domain Concentration

This study addresses the growing concern over the impact of small unmanned aerial vehicles (UAVs), particularly rotor UAVs, on air traffic order and public safety. We propose a novel method for micro-Doppler feature extraction in multi-rotor UAVs within the time-frequency transform domain. Utilizing competitive learning particle swarm optimization (CLPSO), our approach divides population dynamics into three subgroups, each employing unique optimization mechanisms to enhance local search capabilities. This method overcomes limitations in traditional Particle Swarm Optimization (PSO) algorithms, specifically in achieving global optimal solutions. Our simulation and experimental results demonstrate the method’s efficiency and accuracy in extracting micro-Doppler features of rotary-wing UAVs. This advancement not only facilitates UAV detection and identification but also significantly contributes to the fields of UAV monitoring and airspace security.

An estimation method of Unmanned Aerial Vehicle rotor spin frequency based on laser micro-Doppler effect

Accurately estimating the rotor spin frequency of Unmanned Aerial Vehicles (UAVs) is of great significance for UAV detection and identification. However, the existing estimation methods are difficult to accurately estimate the spin frequency in different frequency bands. In this paper, for the laser radar UAV rotor target echo model, a method of estimating the rotor spin frequency of UAV based on a multiple linear regression algorithm is proposed for the first time, which is weight the two existing estimation methods through multiple linear regression algorithm can more accurately estimate the rotor spin frequency of UAV. Experimental results show that the relative error of the estimation method can be reduced from 5.98% and 9.80% to 1.07% after multiple linear regression algorithm processing, which confirms the accuracy of the proposed method, and provides a new method for the micro-Doppler feature extraction of the UAV.

Micro-Doppler Feature Extraction of Rotating Structures of Aircraft Targets with Terahertz Radar

The micro-Doppler features formed by the micro-motion of rotating blades of rotors and turbines are of great significance for aircraft target detection and recognition. Mastering the micro-motion features is the premise of radar target identification. The blades’ length and rotation rate are vital parameters for classifying aircraft targets. One can instantly judge the type and state of the targets by extracting micro-Doppler features. To extract the micro-Doppler features of rotating blades of the turbine target, we utilized microwave-band and terahertz-band radar to simulate the target and extract the Doppler frequency-shift information. For a turbine model with an obvious blade tip structure, we propose an algorithm based on wavelet coefficient enhancement and inverse Radon transform, integrating the time–frequency analysis with image processing. Under low SNR, this method allows for a high-accuracy parameter estimate. For a two-bladed rotor model without an obvious blade tip structure, we conducted an actual measurement experiment on the model utilizing a 120 GHz radar, and we propose a parameter estimation algorithm based on the fitting of the time–frequency distribution. By fitting the data of the time–frequency diagram, the micro-motion characteristic parameters of the rotor target were obtained. The simulation and experimental results demonstrate the benefits of terahertz radar in target detection, and indicate that the proposed algorithms have the characteristics of high extraction precision and insensitivity to noise.

Micro-Doppler Curves Extraction of Space Target Based on Modified Synchro-Reassigning Transform and Ridge Segment Linking

The micro-movement feature is recognized as one of the practical features of space target recognition in academic circles. The separation of the micro-Doppler curve of the scattering center is the key to feature extraction and parameter estimation, which depends on the time–frequency analysis method. The existing techniques have low separation accuracy and adaptability when there are overlap and noise in the time–frequency domain. This paper proposes a micro-Doppler feature extraction algorithm of a space target based on the modified synchro-reassigning transform (MSRT) and ridge segment linking. The MSRT can eliminate repeated assignment problems, has more accurate micro-Doppler frequency estimates than the synchro-reassigning transform, and has lower computational complexity than second-order synchronous compression and synchronous extraction transforms. The re-linking of the ridge realizes the correct connection of the micro-Doppler curves of each scattering center. The simulation data and the electromagnetic calculation data verify the method’s effectiveness.

Range-Max Enhanced Ultrawideband Micro-Doppler Signatures of Behind-the-Wall Indoor Human Motions

In this article, an ultrawideband (UWB) radar is first employed to probe through the opaque wall media to detect behind-the-wall human motions. By employing such a radar, a high-resolution time-range map with different body parts’ reflections highly discriminable in range direction can be obtained. Second, a high-pass filter is applied to remove the wall effects in the raw time-range map. Then, with the aim of exploiting the rich range information so as to enhance their corresponding micro-Doppler features, a novel range-max enhancement strategy is proposed to extract the most significant micro-Doppler feature of each time–frequency cell along range direction for a specific motion. Finally, the effectiveness of the proposed motion feature enhancement strategy is investigated by means of onsite experiments. Comparative classifications using different convolutional neural network (CNN) structures show that the proposed approach outperforms other state-of-the-art micro-Doppler feature extraction methods. The comparison with the narrowband detection case also proves its superiority in feature enhancement in the narrowband detection scene.

Low-SNR Recognition of UAV-to-Ground Targets Based on Micro-Doppler Signatures Using Deep Convolutional Denoising Encoders and Deep Residual Learning

The rapid development of flight control technology has made unmanned aerial vehicles (UAVs) widely used in high-precision strikes on the battlefield. The premise of this is to achieve accurate target recognition using UAV-based radars. Aiming at three typical ground targets, including pedestrians, wheeled vehicles, and tracked vehicles, the micro-Doppler modulation caused by the random vibration of the UAV is analyzed in this article for the first time. To improve the recognition accuracy under low signal-to-noise ratios (SNRs), Doppler signals are transformed into time–frequency images, and a deep convolutional denoising encoder (DCDE) is designed to effectively remove the noise without suppressing micro-Doppler characteristics. To avoid the complicated micro-Doppler feature extraction, deep residual learning that can reduce the burden of network training and gain higher learning efficiency compared with traditional deep convolutional neural networks (DCNNs) is adopted. Recognition results under various occasions using denoised micro-Doppler images and designed residual learning network indicate that the proposed method has higher precision and better robustness than current methods. Even when the SNR is only −16 dB, the overall recognition accuracy still exceeds 90%.

Rotor Blades Micro-Doppler Feature Analysis and Extraction of Small Unmanned Rotorcraft

In this article, the micro-Doppler feature analysis and extraction of small unmanned rotorcraft (SUR) is considered. To be specific, the radar returns from the rotor blades are first modeled as sinusoidal frequency-modulated (SFM) signals. Then, the Gabor transform is utilized to obtain the time-frequency distribution (TFD). In order to solve the problem of limited TF analysis resolutions, high carrier frequency of radar is employed for the sake of separating different sinusoidal curves from TFD. After that, the Hough-Radon transform (HRT) is introduced to detect the sinusoidal curves from the TFD. Finally, based on the relationship between the SFM signal and the rotating blade, the micro-Doppler parameters which can reflect the threat level of the SUR to a large extend are estimated. Compared with other existing methods, the proposed method presents the relationships between the carrier frequency and the rotating blade parameters and it can be employed to extract the micro-Doppler feature of SUR with multiple rotor hubs. Simulation results demonstrate the effectiveness of the proposed micro-Doppler feature extraction method.

Analysis of phase noise influence on micro‐Doppler feature extraction of vibrating target

It is generally considered that increasing the carrier frequency of radar is an important way to improve the precision of micro-motion measurement. However, the increase of the radar frequency may raise the phase noise intensity of the radar transmitting signal and make the extraction more difficult, therefore it is particularly necessary to study the influence of phase noise on the extraction of micro-motion characteristics. In this study, specific studying about the effect of phase noise on the extraction of micro-Doppler (m-D) features is carried out. The effect of phase noise on the extraction performance of the m-D features is evaluated based on a parameter micro-motion signal-to-clutter ratio and an empirical formula is put forward based on the experiments. The results of simulation experiments indicate that increasing the carrier frequency cannot improve the extraction performance of micro-motion features in the case of using both time–frequency analysis method and the new developed sinusoidal frequency modulation Fourier transform (SFMFT) method. Meanwhile, increasing the frequency of target's micro-motion has an improvement effect through time–frequency method due to the increase of Doppler frequency.

Micro‐Doppler feature extraction method for vibration targets based on the segmental intrinsic chirp component decomposition

Based on the three-antenna interferometric radar system, a novel micro-Doppler feature extraction method for vibration targets is proposed in this study. First, the theory of interferometric processing of echo signal for vibration targets is introduced and explained. Second, short-time Fourier transform is conducted with the time–frequency diagram of echo signal obtained. Then, the signals received by three-antenna model are decomposed based on the segmental intrinsic chirp component decomposition. Finally, the 2D trajectory of vibration target is reconstructed and its micro-Doppler features are effectively extracted via the interferometric phase. Besides, the micro-Doppler parameters can also be estimated with the method. Two simulation experiments are conducted to validate the proposed method.

Application of S-Transform Random Consistency in Inverse Synthetic Aperture Imaging Laser Radar Imaging

Under the same principle, laser radar could be more sensitive to the micro-Doppler (m-D) effect due to its wave length, as the characteristic of multi-resolution, S transform could reduce the influence of the micro-Doppler component and enhance the imaging effect. This paper presents a method for micro-Doppler feature extraction in Inverse Synthetic Aperture Imaging Laser Radar (ISAIL) imaging. It is accessible and comprehensive, applying Random Sample Consensus (RANSAC) for the separation and reconstruction of micro-Doppler and rigid body signals. Experiments show that the method can effectively remove the micro-Doppler information and obtain a clear target distance-instantaneous Doppler image.

Micro-Doppler Feature Extraction of Inverse Synthetic Aperture Imaging Laser Radar Using Singular-Spectrum Analysis.

Different from microwave radar, laser radar could be more sensitive to the micro-Doppler (m-D) effect due to its wave length. This limits the application of conventional methods, such as time–frequency based approach, since the processing needs a receiver with much higher sampling frequency than microwave radar. In this paper, a micro-Doppler feature extraction algorithm is proposed for the inverse synthetic aperture imaging laser radar (ISAIL). Singular-spectrum analysis (SSA) is employed for separation and reconstruction of the micro-Doppler and rigid body signal. Clear ISAIL image is obtained by minimum entropy criteria after echo signal decomposition. After theoretical derivation, the computation efficiency and ability of the proposed method is proved by the results of simulation and real data of An-26.

Overlapping Laser Micro-Doppler Feature Extraction and Separation of Weak Vibration Targets

The laser-detected micro-Doppler (MD) effect is more likely to achieve precision target identification and recognition because of its high-accuracy estimation ability. MD features overlapping in the time–frequency (TF) are encountered in targets with similar micromotion parameters, which cannot be solved with one-channel detection. In this letter, a novel separation method based on a constrained particle filter (PF) in a time-varying autoregressive (TVAR) model is developed to solve this extremely underdetermined problem. First, the TVAR model for a multicomponent MD signal is established, and the connection between the interested instantaneous frequency (IF) and model poles is analyzed. Then, the continuity characteristic of the IF law is used to design a constraint condition for a PF assuming that the laser MD effect obeys the sinusoidal frequency modulation form. By fusing the constraint into the process of particle update and weight computation, the IF curve for each component is correctly separated through the well-tracked pole trajectories. Finally, the performances of the presented method and traditional method are compared for a TF overlapping scenario. The simulation results verify the validity and necessity of the new method; meanwhile, the low-level computational complexity makes it possible for real-time processing.

ANALYSIS OF PHASE NOISE INFLUENCE ON MICRO-DOPPLER FEATURE EXTRACTION OF VIBRATING TARGET

It is generally considered that increasing the carrier frequency of radar is an important way to improve the precision of micro-motion measurement. However, the increase of the radar frequency may raise the phase noise intensity of the radar transmitting signal and make the extraction more difficult, therefore it is particularly necessary to study the influence of phase noise on the extraction of micro-motion characteristics. In this study, specific studying about the effect of phase noise on the extraction of micro-Doppler (m-D) features is carried out. The effect of phase noise on the extraction performance of the m-D features is evaluated based on a parameter micro-motion signal-to-clutter ratio and an empirical formula is put forward based on the experiments. The results of simulation experiments indicate that increasing the carrier frequency cannot improve the extraction performance of micro-motion features in the case of using both time–frequency analysis method and the new developed sinusoidal frequency modulation Fourier transform (SFMFT) method. Meanwhile, increasing the frequency of target's micro-motion has an improvement effect through time–frequency method due to the increase of Doppler frequency.

Translational Motion Compensation and Micro-Doppler Feature Extraction of Space Spinning Targets

In order to compensate the translational motion and extract micro-Doppler (m-D) feature of space spinning targets, a novel method based on delayed–conjugated multiplication and m-D compensation is put forward. The translational acceleration is estimated by the delayed–conjugated multiplication processing. A set of m-D basis signals are generated by discretizing the m-D parameter domain. Thus after acceleration compensation processing, the echo is compensated by the m-D basis signals. Furthermore, the m-D feature and residual velocity can be achieved by searching the maximum spectral peak. The high-precision m-D feature extraction with lower computation complexity is achieved by using the proposed method. Finally, the effectiveness of the proposed method is validated by simulations.

Micro-Doppler Feature Extraction Based on Time-Frequency Spectrogram for Ground Moving Targets Classification With Low-Resolution Radar

A novel feature extraction method based on micro-Doppler signature is proposed to categorize ground moving targets into three kinds, i.e., single walking person, two people walking, and a moving wheeled vehicle. Signal models and measured data from a low-resolution radar are first analyzed to find the differences between the micro-Doppler signatures from the three kinds of considered targets. Then, such discriminative micro-Doppler signatures are represented by a 3-D feature vector extracted from the time-frequency spectrograms. In the experiments based on the measured data, the ratio of the between-class distance to the within-class distance, which is defined based on Fisher discriminant analysis, is exploited to assess the discriminative ability of the 3-D feature vector. Moreover, support vector machine classifier is utilized to evaluate the classification performance. Experimental results show that the proposed micro-Doppler features can achieve a good discriminative ability and a satisfactory classification performance.

Micro-Doppler effect analysis and feature extraction in inverse synthetic aperture imaging LADAR imaging

The micro-Doppler (m-D) effect describes the subtle micromotion features of a radar target and provides a new approach for feature extraction and auto radar target recognition. However, the microwave radar cannot provide enough resolution to detect the m-D effect of small targets and long distance targets. In order to obtain high range resolution for the extraction of subtle m-D signatures, inverse synthetic aperture imaging LADAR (ISAIL) is used here. Because the ISAIL uses a frequency modulation continuous wave signal, the m-D effect of ISAIL is different from the microwave radar. In this paper, the m-D effect of ISAIL is analyzed. The features of the m-D signatures in ISAIL are extracted by an improved Hough transform method associated with erosion and dilation operations in binary mathematical morphology. The simulations are given to validate the theoretical analyses and the proposed m-D extraction method. The experiment results show that the ISAIL can offer sufficient information of micromotions when the feature of motions is tiny.

Analysis of radar micro-Doppler signatures from experimental helicopter and human data

This paper highlights the extraction of micro-Doppler (m-D) features from radar signal returns of helicopter and human targets using the wavelet transform method incorporated with time-frequency analysis. In order for the extraction of m-D features to be realised, the time domain radar signal is decomposed into a set of components that are represented at different wavelet scales. The components are then reconstructed by applying the inverse wavelet transform. After the separation of m-D features from the target's original radar return, time-frequency analysis is then used to estimate the target's motion parameters. The autocorrelation of the time sequence data is also used to measure motion parameters such as the vibration/rotation rate. The findings show that the results have higher precision after the m-D extraction rather than before it, since only the vibrational/rotational components are employed. This proposed method of m-D extraction has been successfully applied to helicopter and human data.