Modified particle filtering algorithm for single acoustic vector sensor DOA tracking.

The conventional direction of arrival (DOA) estimation algorithm with static sources assumption usually estimates the source angles of two adjacent moments independently and the correlation of the moments is not considered. In this article, we focus on the DOA estimation of moving sources and a modified particle filtering (MPF) algorithm is proposed with state space model of single acoustic vector sensor. Although the particle filtering (PF) algorithm has been introduced for acoustic vector sensor applications, it is not suitable for the case that one dimension angle of source is estimated with large deviation, the two dimension angles (pitch angle and azimuth angle) cannot be simultaneously employed to update the state through resampling processing of PF algorithm. To solve the problems mentioned above, the MPF algorithm is proposed in which the state estimation of previous moment is introduced to the particle sampling of present moment to improve the importance function. Moreover, the independent relationship of pitch angle and azimuth angle is considered and the two dimension angles are sampled and evaluated, respectively. Then, the MUSIC spectrum function is used as the "likehood" function of the MPF algorithm, and the modified PF-MUSIC (MPF-MUSIC) algorithm is proposed to improve the root mean square error (RMSE) and the probability of convergence. The theoretical analysis and the simulation results validate the effectiveness and feasibility of the two proposed algorithms.

Wind speed and direction measurement method based on intelligent optimization algorithm.

Accurate and rapid measurement of wind speed and direction is an important research topic. However, the current measurement algorithms based on ultrasonic arrays are constrained by the large computational effort caused by the spectrum peak search, which hinders the development and application of ultrasonic array wind parameter measurement technology. To overcome this problem, this study applies an intelligent optimization algorithm for measuring wind speed and direction based on a co-prime arc ultrasonic array, which avoids the problem of a large number of calculations in the spectrum peak search. First, the spatial-spectral function of the propagator method algorithm is employed as the fitness function of the particle swarm optimization algorithm. Then, the wind parameter estimation problem is formulated as a function optimization problem, which realizes the fast and accurate measurement of wind speed and direction. Then, the artificial bee colony algorithm is used to measure wind speed and direction, further reducing the calculation amount of the wind parameter measurement. The performance and speed of the proposed method are verified by the design simulation and comparison experiments, reducing the time complexity by up to 90%. In addition, the feasibility of the proposed method is validated in hardware experiments.