Self-mixing interferometry for rotational speed measurement of servo drives

Sun, Hui; Liu, Ji-Gou; Zhang, Quan; Kennel, Ralph

doi:10.1364/ao.55.000236

Cited by 25 publications

(8 citation statements)

References 38 publications

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“…However, the method's ability must be validated by practical application. Therefore, the method has been tested by using signals generated by "self-mixing" interferometry (SMI), as the precise frequency measurement of SMI signals with low SNR is of great relevance in the research of speed measurement technology [7].…”

Section: Application Example 41 Self-mixing Interferometrymentioning

confidence: 99%

“…Figure 11 shows the schematic structure of the rotational speed measurement using SMI signals. If a turntable is used as the target object, a linear relationship between the frequency of the SMI signal (=Doppler frequency) and the turntable's rotational speed ω can be derived and written as follows [7]:…”

Section: Application Example 41 Self-mixing Interferometrymentioning

confidence: 99%

“…These include accelerometers [3] or piezoelectric sensors [4,5]. Interferometers such as Laser Doppler Velocimeters or "selfmixing" interferometers use signal frequencies for the measurement of physical parameters such as vibration or velocity [6,7]. Therefore, precise frequency measurement is of the utmost importance in measurement technologies [8].…”

Section: Introductionmentioning

confidence: 99%

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Frequency Measurement Method of Signals with Low Signal-to-Noise-Ratio Using Cross-Correlation

Liu

Liu²,

Kennel

2021

Machines

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Precise frequency measurement plays an essential role in many industrial and robotic systems. However, different effects in the application’s environment cause signal noises, which make frequency measurement more difficult. In small signals or rough environments, even negative Signal-to-Noise Ratios (SNRs) are possible. Thus, frequency measuring methods, which are suited for low SNR signals, are in great demand. While denoising methods such as autocorrelation do not suffice for small signal with low SNR, frequency measurement methods such as Fast-Fourier Transformation or Continuous Wavelet Transformation suffer from Heisenberg’s uncertainty principle, which makes simultaneous high frequency and time resolutions impossible. In this paper, the cross-correlation spectrum is presented as a new frequency measuring method. It can be used in any frequency domain, and provides greater denoising than autocorrelation. Furthermore, frequency and time resolutions are independent from one another, and can be set separately by the user. In simulations, it achieves an average deviation of less than 0.1% on sinusoidal signals with a SNR of −10 dB and a signal length of 1000 data points. When applied to “self-mixing”-interferometry signals, the method can reach a normalized root-mean square error of 0.2% with the aid of an estimation method and an averaging algorithm. Therefore, further research of the method is recommended.

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Section: Application Example 41 Self-mixing Interferometrymentioning

confidence: 99%

Section: Application Example 41 Self-mixing Interferometrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Frequency Measurement Method of Signals with Low Signal-to-Noise-Ratio Using Cross-Correlation

Liu

Liu²,

Kennel

2021

Machines

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“…Self-mixing or optical feedback interferometry based laser diode sensors [1]- [3] are being increasingly used for metric sensing applications due to their low-cost, auto-aligned and compact nature [4], [5]. Multiple SMI applications such as micro-displacement measurement [6], measurement of rotation angles (yaw and pitch) [7], detection of single micro-particles in airflow [8], displacement measurement in resonant gyroscopes [9], acquisition of arterial pulse wave [10], Young's modulus measurement [11], measurement of rotational speed of servo drives [12], measurement…”

Section: Introductionmentioning

confidence: 99%

Self-Mixing Interferometric Signal Enhancement Using Generative Adversarial Network for Laser Metric Sensing Applications

2019

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Measurement performance of self-mixing interferometric (SMI) laser sensor can be significantly affected due to the presence of noise. In this case, conventional signal enhancement techniques yield compromised performance due to several limitations which include processing signals in frequency domains only, relying mainly on first order statistics, loss of important information present in higher frequency band and handling limited number of noise types. To address these issues, we propose a solution based on using generative adversarial network, a popular deep learning scheme, to enhance SMI signal corrupted with different noise types. Thus, taking advantage of the deep networks that can learn arbitrary noise distribution from large example set, our proposed method trains the deep network model end-to-end, able to process raw waveforms directly, learn 51 different noise conditions including white noise and amplitude modulation noise for 1,140 different types of SMI waveforms made up of 285 different optical feedback coupling factor (C) values and 4 different line-width enhancement factor α values. The results show that the proposed method is able to significantly improve the SNR of noisy SM signals on average of 19.49, 16.29, 10.34 dB for weak-, moderate-, and strong-optical feedback regime signals, respectively. For amplitude modulated SMI signals, the proposed method has corrected the amplitude modulation with maximum error (using area-under-thecurve based quantitative analysis) of 0.73% for SMI signals belonging to all optical feedback regimes. Thus, our proposed method can effectively reduce the noise without distorting the original signal. We believe that such a unified and precise method leads to enhancement of performance of SMI laser sensors operating under real-world, noisy conditions. INDEX TERMS Interferometry laser sensors, self-mixing signal enhancement, vibration measuring laser sensors, waveform enhancement, generative adversarial network (GAN), signal noise removal, neural network for signal enhancement.

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“…The reflected or scattered light is injected back into the laser cavity, superimposed on the existing laser cavity field, and interferes with the existing field. The research on optical feedback during recent decades has led to an interesting field for a new practical application, laser self-mixing interferometry (SMI) [1][2][3][4][5][6]. It was first proposed in 1968 for use as a Doppler velocimeter with a gas laser [7].…”

Section: Introductionmentioning

confidence: 99%

A novel algorithm for laser self-mixing sensors used with the Kalman filter to measure displacement

Sun

Liu²

2018

Meas. Sci. Technol.

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This paper proposes a simple and effective method for estimating the feedback level factor C in a self-mixing interferometric sensor. It is used with a Kalman filter to retrieve the displacement. Without the complicated and onerous calculation process of the general C estimation method, a final equation is obtained. Thus, the estimation of C only involves a few simple calculations. It successfully retrieves the sinusoidal and aleatory displacement by means of simulated self-mixing signals in both weak and moderate feedback regimes. To deal with the errors resulting from noise and estimate bias of C and to further improve the retrieval precision, a Kalman filter is employed following the general phase unwrapping method. The simulation and experiment results show that the retrieved displacement using the C obtained with the proposed method is comparable to the joint estimation of C and α. Besides, the Kalman filter can significantly decrease measurement errors, especially the error caused by incorrectly locating the peak and valley positions of the signal.

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Self-mixing interferometry for rotational speed measurement of servo drives

Cited by 25 publications

References 38 publications

Frequency Measurement Method of Signals with Low Signal-to-Noise-Ratio Using Cross-Correlation

Frequency Measurement Method of Signals with Low Signal-to-Noise-Ratio Using Cross-Correlation

Self-Mixing Interferometric Signal Enhancement Using Generative Adversarial Network for Laser Metric Sensing Applications

A novel algorithm for laser self-mixing sensors used with the Kalman filter to measure displacement

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