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The existing variable frequency control method for local ventilator lacks prediction of gas outburst variable. When a large amount of gas emission abnormally, there is a certain lag in regulation, which is easy to lead to gas accumulation. To solve this problem, a variable frequency control system for local ventilator based on fuzzy theory is designed. Fuzzy control is realized by using gas fuzzy controller and air volume fuzzy controller. The control quantity output by two fuzzy controllers is compared. The frequency conversion situation of ventilator is determined according to the larger value. When the two are equal, the fuzzy control of gas is dominant. The classification method based on gas emission is adopted. With the air volume corresponding to the farthest working point as the auxiliary, the ventilator frequency is divided into 4 levels. The gas volume fraction of the heading working face reaching 0.8% is set as the frequency-increasing condition. The gas volume fraction not more than 0.6% or 0.5% is set as the frequency-reducing condition. Moreover, the air supply quantity of the ventilator after frequency reduction is set as the air supply volume required to control the gas volume fraction of return airflow at 0.7% or 0.6% when the frequency reduction condition is achieved. When a large amount of abnormal gas emission, the ventilator is increased in frequency to reduce the gas concentration. At the same time, the air supply volume of the ventilator can meet the greater gas discharge demand. The ventilator can provide a certain buffer for adjustment, and overcome the shortcomings of frequency conversion control lag. The test results show that the gas volume fraction is 0.5% under the condition of frequency reduction. The air supply volume after frequency reduction is the air supply volume required to control the gas volume fraction of return air at 0.6% when the frequency reduction condition is achieved. The control effect is good under this condition. But the air supply volume of level I is slightly less than the minimum air supply volume required at the farthest heading distance. The new frequency level I* between level I and level II can be set. The air supply volume can be increased by increasing the frequency of the ventilator to meet the minimum air supply volume requirement at the farthest heading distance.

The performance of traditional direction of arrival (DOA) estimation methods always deteriorates at a low signal-to-noise ratio (SNR) or without sufficient observations. This paper investigates the Bayesian DOA estimation problem aided by the prior knowledge from the target tracker. The Bayesian Cramér–Rao lower bounds (CRLB) and the expected CRLB are first derived to evaluate the theoretical performance of Bayesian DOA estimation. Based on the maximum a posterior (MAP) estimator in the Bayesian framework, two methods are proposed. One is a two-step grid search method for a single target DOA case. The other is a gradient-based iterative solution for multiple targets DOA case, which extends the traditional Newton method by incorporating the prior knowledge. We also propose a minimum mean square error (MMSE) estimator using a Monte Carlo method, which requires trading off accuracy against computational complexity. By comparing with the maximum likelihood (ML) estimators and the MUSIC algorithm, the proposed three Bayesian estimators improve the DOA estimation performance in low SNR or with limited snapshots. Moreover, the performance is not affected by the correlation between sources. [ABSTRACT FROM AUTHOR]