Robust tracking of moving sound source using scaled unscented particle filter

Tracking an active sound source involves the modeling of non-linear non-Gaussian systems. To address this problem, this paper proposed scaled unscented particle filter (SUPF) algorithm for tracking moving sound source. The particle filter part of the SUPF provides the general probabilistic framework to handle non-linear non-Gaussian systems, and the scaled unscented Kalman filter (SUKF) part of the SUPF generates better proposal distributions by taking into account the most recent observation. Meanwhile, models used in SUPF algorithm were also explored for the sound source motion, observation and the likelihood of the sound source location in the light of the Langevin process. Compared with the conventional PF approach, the simulated results demonstrated that the SUPF algorithm had superior tracking performance.     

Study on fast linear scanning for a new laser scanner

In the field of lidar system design, there is a need for laser scanners that offer fast linear scanning, are small size and have small a rotational inertia moment. Currently, laser scanners do not meet the above needs. A new laser scanner based on two amplified piezoelectric actuators is designed in this paper. The laser scanner has small size, high mechanical resonance frequencies and a small rotational inertia moment. The size of the mirror is 20 mm×15 mm. To achieve fast linear scanning performance, an open-loop controller is designed to compensate the hysteresis behavior and to restrain oscillations that are caused by the mechanical resonances of the scanner's mechanical structure. By comparing measured scanning waveforms, nonlinearities and scan line images between the uncontrolled and controlled scanner, it was found that the scanning linearity of linear scanning was improved The open-loop controlled laser scanner realizes linear scanning at 250 Hz with optical scan angle of ±12 mrad.