Self-mixing interferometry (SMI) is an efficient technique applied to measure distance, velocity, displacement, and vibration. In this work, a compact and low cost SMI is applied to measure the rotational speed of a servo drive up to 6000 RPM. The application of SMI to rotational speed measurement of servo drives instead of the usage of incremental encoders is proposed. The Doppler frequency is obtained via analysis on the power spectral density, which is estimated by the smoothing periodogram method based on the fast Fourier transformation. The signals are processed in MATLAB and LABVIEW, showing that the SMI can be applied to dynamic rotational speed measurement of servo drives. Results of experiments demonstrate that this system is implementable for rotational speed measurement over the whole range from 3 RPM to 6000 RPM. In addition, the system used to measure rotational speed can also accurately record changes in position without integrating the velocity.
An important prerequisite for the radar network detection is that the measurements from local radars are transformed to a common reference frame without systematic or registration errors. For the signal level alignment, only partial signals are available for global decision-making due to power and bandwidth limitations. In this paper, a low-communication-rate spatial alignment in range-Doppler domain is proposed for networked radars without the prior spatial information (positions and attitudes) of radars, which is different from the existing methods in the trajectory domain or echo domain for alignment. To reduce the radar-to-fusion-center communication-rate, the method of initial constant false alarm rate detection is used to censor the signals in range-Doppler domain from local radars. Based on the spatial alignment model for the networked radars in geometry, a maximization problem is formulated. The objective function is the cross-correlation between the range-Doppler domain signals from different local radars. The optimization problem is solved by a genetic algorithm. Simulation results show that the rotation matrix and translation vector are estimated, and the detection probability of the proposed algorithm is improved after alignment and fusion compared with state-of-art methods.
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