Fringe Detection and Displacement Sensing for Variable Optical Feedback-Based Self-Mixing Interferometry by Using Deep Neural Networks.

Laser feedback-based self-mixing interferometry (SMI) is a promising technique for displacement sensing. However, commercial deployment of such sensors is being held back due to reduced performance in case of variable optical feedback which invariably happens due to optical speckle encountered when sensing the motion of non-cooperative remote target surfaces. In this work, deep neural networks have been trained under variable optical feedback conditions so that interferometric fringe detection and corresponding displacement measurement can be achieved.

Toward an Estimation of the Optical Feedback Factor on the Fly for Displacement Sensing.

In this paper, a method based on the inherent event-based sampling capability of laser optical feedback interferometry (OFI) is proposed to assess the optical feedback factor when the laser operates in the moderate and strong feedback regimes. Most of the phase unwrapping open-loop OFI algorithms rely on the estimation of to retrieve the displacement with nanometric precision. Here, the proposed method operates in open-loop configuration and relies only on OFI's fringe detection, thereby improving its robustness and ease of use.

Classification of laser modality for a self-mixing interferometric sensor.

Robust detection and classification of multimodal self-mixing (SM) signals emanating from the optical feedback-based SM interferometric laser sensor are necessary for accurate retrieval of sensing information. An abrupt shift in the modality of SM signals can occur due to different operating conditions; consequently, an unidentified modality shift can cause severe measurement errors. Therefore, it is necessary to detect and identify the type of multimodality so that relevant adjustments could then be made, either in the SM sensor setup or in the relevant signal processing, to avoid the errors caused by the shift in modality.

Multimodal fringe detection for a self-mixing interferometry-based vibration sensor.

Robust detection of interferometric fringes is critical for accurate sensing by self-mixing interferometric (SMI) displacement sensors. Mode-hopping of a laser diode (LD) can potentially diversify SMI fringes, transforming them from mono-modal to multimodal. Thus, fringe detection of a multimodal SMI signal becomes a bigger challenge as the relative strength of each mode may be different, leading to further diversity in the fringes belonging to each regime.

Classification of laser self-mixing interferometric signal under moderate feedback.

This paper presents a different approach to classify self-mixing (SM) signals operating in the moderate feedback regime. A total of six distinct classes of SM signals can be defined based on the SM inherent shapes, which depend on both the feedback factor C and the linewidth enhancement factor α. This classification allows clear identification of SM signals for which normalization issues can arise and thus for which displacement precision is inherently reduced due to the very nature of the signal itself.

MEMS accelerometer embedded in a self-mixing displacement sensor for parasitic vibration compensation.

A self-mixing (SM) laser displacement sensor coupled with a microelectromechanical system (MEMS) accelerometer is presented that enables reliable displacement measurements even in the case of a nonstationary laser head. The proposed technique allows the use of SM-based sensors for embedded applications. The system resolution is currently limited to approximately 300 nm due to the noise characteristics of the currently used accelerometer.