Variance‐SNR Based Noise Suppression on Linear Canonical Choi‐Williams Distribution of LFM Signals

ZHANG, Zhichao

doi:10.1049/cje.2020.00.367

Cited by 2 publications

(1 citation statement)

References 108 publications

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“…Linear frequency modulated (LFM) signal is widely used in complex electromagnetic environment due to its strong anti-interference ability, low probability of interception, large bandwidth and other advantages [1][2][3]. In electronic reconnaissance, important intelligence information can be obtained by analyzing the intercepted enemy radar signals.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of LFM Signal Based on RD Compressed Sampling and DFRFT Dictionary

Meng

Wang

2022

Preprint

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In order to estimate the parameters of linear frequency modulation (LFM) signals directly from compressed sampled data, a method combining random demodulator (RD) and discrete fractional Fourier transform (DFRFT) dictionary is proposed in this manuscript. Firstly, according to the characteristics of LFM signal, the RD system parameters are adjusted to obtain effective compressed sampling data; Then, based on the fractional Fourier transform theory, a DFRFT dictionary is constructed, and sparse representation coefficients are obtained by reconstructing the compressed sampling data through the recovery algorithm and DFRFT dictionary. The signal has the characteristics of impact under the optimal fractional transform order, so the problem of signal parameter estimation can be converted into searching the location of the maximum value of sparse representation coefficients, and the location is determined by parameter optimization algorithm, then the initial frequency and frequency modulation (FM) slope of LFM signal can be estimated. Finally, simulation and real data experiments are carried out to verify that the proposed method not only has a good sparse representation ability for LFM signals, but also can directly be used to estimate the parameter of LFM signals with compressed sampling data.

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Section: Introductionmentioning

confidence: 99%

Parameter Estimation of LFM Signal Based on RD Compressed Sampling and DFRFT Dictionary

Meng

Wang

2022

Preprint

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Intelligent Identification and Classification of Small UAV Remote Control Signals Based on Improved Yolov5-7.0

Li,

Hao,

Wang

et al. 2024

IEEE Access

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At present, an increasing number of small UAVs(Unmanned Aerial Vehicles) are commercialized and common, and the application of small UAVs has very good development prospects, such as UAV distribution services, UAV aerial photography services, and UAV formation performance. However, the misuse of small drones poses a significant threat. Lawbreakers use small drones equipped with various sensors to spy on personal privacy, steal corporate secrets, and threaten national conferences, which have had many adverse effects on society. In future wars, these small drones will perhaps be used on the battlefield along with high-end weapons. Therefore, it is necessary to find a solution for effectively identifying the basic information of UAV. For the existence of UAV and various small UAV types, this paper proposes a combination of RF sensing and target detection techniques with target detection algorithms to learn RF signal frequency frequency hopping features to detect UAV presence and identify the detected UAV. First, the RF signal of the UAV was obtained in real time by a software radio, and the time-frequency analysis of the short-time Fourier transform and wavelet transform is performed to generate frequency domain images with retained frequency hopping features. Then, the improved Yolov5-7.0 target detection model was employed for training, and finally, the trained model was used for identification and classification. The results showed that the method can effectively assist the detection and classification of UAVs that were obtained by identifying and classifying 5400 unlabeled images. The F1 score was 0.93, and the three assessment measures of P(Precision), R(Recall), and mAP(mean Average Precision) were 1.00, 1.00, and 0.967, respectively.INDEX TERMS Small UAV, RF sensing, object detection, frequency hopping feature, short-time Fourier transform, wavelet transform, software radio.

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Variance‐SNR Based Noise Suppression on Linear Canonical Choi‐Williams Distribution of LFM Signals

Cited by 2 publications

References 108 publications

Parameter Estimation of LFM Signal Based on RD Compressed Sampling and DFRFT Dictionary

Parameter Estimation of LFM Signal Based on RD Compressed Sampling and DFRFT Dictionary

Intelligent Identification and Classification of Small UAV Remote Control Signals Based on Improved Yolov5-7.0

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