Anthony Sourice

Real-time tracking of time-varying velocity using a self-mixing laser diode

A new method is proposed for estimating the time-varying velocity of a moving target with a low-cost laser sensor using optical feedback interferometry. A new algorithm is proposed to track velocity variations from real-time analysis of the output signal of a self-mixing laser diode. This signal is strongly corrupted by a multiplicative noise caused by the speckle effect, which occurs very often with noncooperative targets used in many industrial applications. The proposed signal processing method is based on a second order adaptive linear predictor filter, which enables us to track the digital instantaneous Doppler frequency, which is proportional to the velocity. A model of the laser diode output signal is proposed, and it is shown that the sensor and its associated algorithm have a global first-order lowpass transfer function with a cutoff frequency expressed as a function of the speckle perturbations, the signal to noise ratio and the mean Doppler frequency. Numerical as well as experimental results illustrate the properties of this sensor.

Real-time parametric estimation of velocity using optical feedback interferometry

A low-cost laser sensor using optical feedback interferometry has been designed to measure velocities. With digital signal processing based on an order two autoregressive model of the optical power, an inaccuracy of about 0.5% can be reached.

Red blood cell velocity estimation in microvessels using the spatiotemporal autocorrelation

This paper deals with the problem of red blood cell velocity measurement in microvessels using dynamic video microscopy. More precisely, the problem of one-dimensional red blood cell velocity estimation is addressed, using algorithms based on the spacetime image obtained when a single line from the video, taken inside and along the vessel axis, is mapped into an image as a function of time. Flowing red blood cells generate an oriented and textured spatiotemporal plane, the orientation of which is related to their velocity. In order to perform space- and time-localized velocity estimations, orientation estimations have to be done locally in the spatiotemporal plane. Therefore, we propose the use of the autocorrelation sequence, the orientation of which is also related to the velocity. The region of interest of the autocorrelation, containing the velocity information, is selected using a watershed algorithm. We finally suggest two different algorithms estimating the spatiotemporal plane orientation upon the aforementioned region. Results and comparisons of these methods are proposed, using a controlled experiment and real-life blood flow video sequences.

Autocorrelation fitting for texture orientation estimation

The problem of short-term texture orientation is dealt with in this paper through autocorrelation fitting. Actually, the computation of the estimated autocorrelation sequence from a textured oriented image brings about the apparition of an oriented pattern. This pattern is looked upon, in this work, as a Gaussian curve, the parameters of which give an estimated orientation. Those parameters are estimated by a two-dimensional (2-D) curve fitting on a particular region of the autocorrelation. The determination of this region is done automatically thanks to watersheds. The algorithm is developed for synthetic textured images but appears to be effective when applied to real life oriented images.

Two-dimensional frequency estimation using autocorrelation phase fitting

The problem of two-dimensional (2D) frequency estimation of a complex sinusoid embedded in a additive white Gaussian noise is addressed. A new frequency estimator based on a least squares plane fitting of the estimated autocorrelation phase of the signal is derived. This algorithm requires a 2D phase unwrapping step which can be easily done. This algorithm is shown to be unbiased and attains the Cramer Rao bounds for high signal-to-noise ratio (SNR > 0 dB). Accuracy and robustness of this new 2D frequency estimator are statistically assessed by Monte Carlo simulations. The results obtained show that a good local frequency estimation can be achieved with a very simple algorithm, and a very small number of points are used for the autocorrelation estimation.

Accurate and real-time Doppler frequency estimation with multiplicative noise for velocity measurements using optical feedback interferometry

This paper proposes to analyze a real-time signal processing algorithm for on-line velocity measurement of a moving target, with a laser sensor using optical feedback interferometry. This velocity is based on an accurate estimation of the Doppler frequency from a spectral analysis of the laser diode output signal. This study takes into account both main causes of disrupting noises: additive noise and multiplicative noise caused by the speckle effect. A very simple expression is given for the Doppler frequency estimation bias based on an autoregressive spectral analysis method intensive Monte Carlo simulations, which are compared with experimental results, show that relative bias and standard deviations of about 0.1% and 0.5% respectively can be obtained for a wide range of velocities.

Amplitude and Phase Drift Correction of EFPI Sensor Systems Using Both Adaptive Kalman Filter and Temperature Compensation for Nanometric Displacement Estimation

Nanometric displacement measurements by Extrinsic Fiber Fabry-Perot interferometers (EFPI) is extremely susceptible to external environmental changes. Temperature, in particular, has a remarkable influence on the optical power and wavelength of the laser diode in use, in addition to the thermal expansion of the mechanical structure. In this paper we propose an optimization of the EFPI sensor in order to use it for very long-term (more than one year) and for high-precision displacement measurements. For this purpose, a real time and adaptive estimation procedure based on a homodyne technique and a Kalman filter is established. During a sinusoidal laser diode current modulation, the Kalman filter provides a correction of the amplitude drift caused by the resultant optical power modulation and external perturbations. Besides, stationary temperature transfer operators are estimated via experimental measurements to reduce the additive thermal noise induced in the optical phase and mechanical components.

A low-cost optical feedback interferometer for real-time velocity measurement

A low-cost laser sensor using optical feedback interferometry has been designed to measure velocities. With a digital signal processing based on an order 2 autoregressive model of the optical power, an accuracy of about 0.5% can be reached.

Real time and adaptive Kalman filter for joint nanometric displacement estimation, parameters tracking and drift correction of EFFPI sensor systems

This paper deals with the optimization of an Extrinsic Fiber Fabry-Pérot Interferometer used for very long term (more than one year) and high precision displacement measurements by a real time and adaptive estimation procedure based on a Kalman filter. By performing a sinusoidal laser diode current modulation, a wavelength modulation is created. The Kalman filter takes into account not only the correction of the measurement drift caused by the resultant Optical Power Modulation, but also the correction of the measurement noise and temperature fluctuations. The tracking algorithm is presented, the complete system has been set up, the Kalman filter and the demodulation are programmed on an FPGA board. Experimental results give an estimation error of about 2nm for a 7000nm displacement.

Modeling and analysis of speckle effects for velocity measurements with Self-Mixing Laser Diode Sensors

This paper deals with the optimization of laser diode self mixing sensors for velocity measurements when self-mixing signals are corrupted by the speckle phenomenon, a process of a random nature due to the surface roughness of the moving target. A new behavioral model is presented and used to design and compare real time and optimal signal processing algorithms for Doppler frequency estimations. Finally, experimental results, obtained in the context of automotive applications, validate the proposed model in measuring car velocities.