Rotor UAV’s Micro-Doppler Signal Detection and Parameter Estimation Based on FRFT-FSST

Hou, Huayan; Yang, Zhiliang; Pang, Cunsuo

doi:10.3390/s21217314

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…FSST Short-time Fourier transform (STFT) is a transform method that adds window processing to the Fourier transform to be able to improve the local localization ability of Fourier analysis on the local expression in the time domain [3] . The Fourier synchronous compressive transform method is based on the STFT method [4] , which extracts its instantaneous frequency and converts the transform coefficients in the time-space domain to the time-frequency domain [8] .…”

Section: Fsst Ridge Characterizationmentioning

confidence: 99%

Naive Bayesian classifier target recognition based on ridge features

Shi,

Yue

2023

J. Phys.: Conf. Ser.

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Traditional time-frequency algorithms do not have a high feature recognition rate for targets in a low signal-to-noise background, and the number of operations for classification using plain Bayes is large and slow. To solve the above problems and achieve recognition of different ship echo signals in water, this paper proposes a target recognition method based on time-frequency ridge features by using collected audio data of different ship types, combined with Fourier Simultaneous Compression Transform (abbreviation: FSST), starting from time-frequency features, by extracting the FSST time-frequency ridge and finally using the Hopper Parsimonious Bayes classifier to obtain respectively. The recognition rate of ships under FSST and the recognition rate of ships based on ridge line features are obtained respectively. The final comparison shows that the correct recognition rate of the plain Bayesian classifier using the spine features of the time-frequency map is much higher than that of the conventional FSST time-frequency method, and it also outperforms the FSST in terms of computational power and speed.

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Section: Fsst Ridge Characterizationmentioning

confidence: 99%

Naive Bayesian classifier target recognition based on ridge features

Shi,

Yue

2023

J. Phys.: Conf. Ser.

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“…In order to observe the micro-Doppler effect of UAV rotor blades and extract characteristic parameters, a theoretical micro-Doppler echo model of UAV rotor blades in radar detection systems was firstly established [ 7 ]. Time–frequency analysis is a common method to extract features from echoes, including short time Fourier transform (STFT) [ 8 ], Gabor transform [ 9 ], Wigner-Ville distribution (WVD) [ 10 ] and wavelet transform [ 11 ]. STFT adopts the method of intercepting the time window.…”

Section: Introductionmentioning

confidence: 99%

Micro-Doppler Signature Detection and Recognition of UAVs Based on OMP Algorithm

Fan,

Wu,

et al. 2023

Sensors

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With the proliferation of unmanned aerial vehicles (UAVs) in both commercial and military use, the public is paying increasing attention to UAV identification and regulation. The micro-Doppler characteristics of a UAV can reflect its structure and motion information, which provides an important reference for UAV recognition. The low flight altitude and small radar cross-section (RCS) of UAVs make the cancellation of strong ground clutter become a key problem in extracting the weak micro-Doppler signals. In this paper, a clutter suppression method based on an orthogonal matching pursuit (OMP) algorithm is proposed, which is used to process echo signals obtained by a linear frequency modulated continuous wave (LFMCW) radar. The focus of this method is on the idea of sparse representation, which establishes a complete set of environmental clutter dictionaries to effectively suppress clutter in the received echo signals of a hovering UAV. The processed signals are analyzed in the time–frequency domain. According to the flicker phenomenon of UAV rotor blades and related micro-Doppler characteristics, the feature parameters of unknown UAVs can be estimated. Compared with traditional signal processing methods, the method based on OMP algorithm shows advantages in having a low signal-to-noise ratio (−10 dB). Field experiments indicate that this approach can effectively reduce clutter power (−15 dB) and successfully extract micro-Doppler signals for identifying different UAVs.

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“…Fast and reliable disentangling of natural signals is a necessary step for a variety of applications including biomedicine, audio, seismic applications, and radar [ 1 , 2 ]. Such complex signals are often generated by physical systems that can be modeled as a superimposition of Amplitude-and Frequency-Modulated (AM-FM) waves (or modes), and referred to as MultiComponent Signal (MCS).…”

Section: Introductionmentioning

confidence: 99%

Pseudo-Bayesian Approach for Robust Mode Detection and Extraction Based on the STFT

Legros

Fourer

2022

Sensors

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This paper addresses the problem of disentangling nonoverlapping multicomponent signals from their observation being possibly contaminated by external additive noise. We aim to extract and to retrieve the elementary components (also called modes) present in an observed nonstationary mixture signal. To this end, we propose a new pseudo-Bayesian algorithm to perform the estimation of the instantaneous frequency of the signal modes from their time-frequency representation. In a second time, a detection algorithm is developed to restrict the time region where each signal component behaves, to enhance quality of the reconstructed signal. We finally deal with the presence of noise in the vicinity of the estimated instantaneous frequency by introducing a new reconstruction approach relying on nonbinary band-pass synthesis filters. We validate our methods by comparing their reconstruction performance to state-of-the-art approaches through several experiments involving both synthetic and real-world data under different experimental conditions.

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Rotor UAV’s Micro-Doppler Signal Detection and Parameter Estimation Based on FRFT-FSST

Cited by 6 publications

References 35 publications

Naive Bayesian classifier target recognition based on ridge features

Naive Bayesian classifier target recognition based on ridge features

Micro-Doppler Signature Detection and Recognition of UAVs Based on OMP Algorithm

Pseudo-Bayesian Approach for Robust Mode Detection and Extraction Based on the STFT

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