A Review of Signal Processing Techniques for Continuous-Scan Laser Doppler Vibrometry

Author(s):  
Shifei Yang ◽  
David Ehrhardt ◽  
Matthew S. Allen

A Laser Doppler Vibrometer (LDV) measures the laser Doppler frequency shift and converts it to the velocity at a point of a structure along the laser beam direction. In commercially available scanning LDV, the laser is redirected by a pair of orthogonal mirrors from one point to another, measuring the responses at these points sequentially. Continuous-Scan Laser Doppler Vibrometry (CSLDV) is built on scanning LDV; the laser sweeps continuously over a structure while recording the response along the laser path. The continuous-scan approach can greatly accelerate modal testing, providing spatially detailed vibration shape of the structure at tens or even hundreds of points in the time that is required to measure the vibration at a single point. However, extracting vibration shapes from CSLDV measurements is challenging because the laser spot is continuously moving. This technical difficulty and the equipment cost have become the major barriers that prevent the widespread use of CSLDV. Several algorithms to extract vibration shapes have been developed since CSLDV was introduced. Ewins et al proposed a polynomial approach that treats the vibration shape along the laser scan path as a polynomial function of the laser position. The polynomial coefficients were found from the sideband harmonics in the frequency spectrum of the acquired velocity signal. Allen et al proposed a lifting approach that collects the measured responses at the same location along the laser path. The reorganized measurements appear to be from a set of pseudo transducers attached to the structure. Hence, the well-established conventional modal identification routines can be applied to process CSLDV measurement. Algorithms based on linear time periodic system identification theory were explored as well. These algorithms are based on the fact that the measured velocities along the laser path are the responses of a special liner time periodic system when a closed, periodic laser scan pattern is employed. For the first time, this work compares these signal processing techniques employed in different applications using the same set of data obtained from a cantilever beam. The noise and uncertainty in the reconstructed vibration shapes are discussed in order to present the advantages and disadvantages of each method.

Author(s):  
A. D. Brown ◽  
R. A. Cookson

The laser-Doppler, fibre-optic probe which has been developed at Cranfield and which can be used for the non-intrusive detection and measurement of mechanical vibration, has been improved optically and by the inclusion of a microprocessor system to replace the previously employed frequency tracker and Bragg cells. These improvements facilitate the manufacture of a laser-Doppler probe which is more compact and considerably cheaper than the previous version, and which has potential for the application of a wide range of signal processing techniques.


2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.


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