Large dynamic range time-frequency signal analysis with application to helicopter Doppler radar data

Marple, S.L.; Marino, C.S.; Strange, Shawn

doi:10.1117/12.507408

Cited by 7 publications

(5 citation statements)

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“…The experimental data used in the analysis that follows is of a hovering helicopter. For a helicopter, the main rotor blades, the tail rotor blades, and the hub have unique signatures suitable for target identification [9,[26][27]. Generally, radar returns from a helicopter are back-scattered from the fuselage, the rotor blades, the tail blades, and the hub among other structures.…”

Section: A Helicopter Datamentioning

confidence: 99%

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

Thayaparan¹,

Abrol

Riseborough³

et al. 2007

IET Radar Sonar Navig.

281

153

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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 realized, 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.

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Section: A Helicopter Datamentioning

confidence: 99%

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

Thayaparan¹,

Abrol

Riseborough³

et al. 2007

IET Radar Sonar Navig.

281

153

View full text Add to dashboard Cite

show abstract

“…The signal is considered within the interval of 400 ms, sampled with a rate of ∆t = 1/48000 s. Four sinusoidal components, caused by the rigid body, are at the frequencies −10.3 kHz, −2.5 kHz, 2.3 kHz and 2.7 kHz. Two components at −0.4 kHz and 0.4 kHz correspond to the modulated time tones commonly added to the data tape [12]. The sinusoidal FM signals, corresponding to the rotation of the main rotor blades, are modeled as:…”

Section: Uh-1d Helicoptermentioning

confidence: 99%

“…The SNR in this case is 3 dB. To compare our simulated radar image with the real one, refer to [4], [12].…”

Section: Uh-1d Helicoptermentioning

confidence: 99%

Micro-Doppler parameter estimation from a fraction of the period

Thayaparan

Stanković

Daković

et al. 2010

IET Signal Process.

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“…Most researchers study the PHM of a HTS based on vibration signals. The vibration signals are processed and the fault types are recognized by using expert experience or machine learning methods [ 7 , 8 , 9 ]. Although the vibration signal of a helicopter carries rich running state information and can be used to detect early weak faults of mechanical components in time, HUMS cannot effectively and reliably diagnose some parts, such as the bearings of HMGB, from the vibration signal [ 10 ], because HMGB-status monitoring based on vibration signal analysis will encounter the following challenges: (1) The early fault features contained in the vibration signal is usually weak, and it will be interfered with by various noises in the transmission process.…”

Section: Introductionmentioning

confidence: 99%

A Fault Detection Method Based on an Oil Temperature Forecasting Model Using an Improved Deep Deterministic Policy Gradient Algorithm in the Helicopter Gearbox

Wei

Cheng

et al. 2022

Entropy

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The main gearbox is very important for the operation safety of helicopters, and the oil temperature reflects the health degree of the gearbox; therefore establishing an accurate oil temperature forecasting model is an important step for reliable fault detection. Firstly, in order to achieve accurate gearbox oil temperature forecasting, an improved deep deterministic policy gradient algorithm with a CNN–LSTM basic learner is proposed, which can excavate the complex relationship between oil temperature and working condition. Secondly, a reward incentive function is designed to accelerate the training time costs and to stabilize the model. Further, a variable variance exploration strategy is proposed to enable the agents of the model to fully explore the state space in the early training stage and to gradually converge in the training later stage. Thirdly, a multi-critics network structure is adopted to solve the problem of inaccurate Q-value estimation, which is the key to improving the prediction accuracy of the model. Finally, KDE is introduced to determine the fault threshold to judge whether the residual error is abnormal after EWMA processing. The experimental results show that the proposed model achieves higher prediction accuracy and shorter fault detection time costs.

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Large dynamic range time-frequency signal analysis with application to helicopter Doppler radar data

Cited by 7 publications

References 0 publications

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

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

Micro-Doppler parameter estimation from a fraction of the period

A Fault Detection Method Based on an Oil Temperature Forecasting Model Using an Improved Deep Deterministic Policy Gradient Algorithm in the Helicopter Gearbox

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