In order to improve the location accuracy of passive sound source location technology in a complex environment, an improved particle swarm optimization algorithm is proposed. Aiming at the nonlinear optimization problem in the time difference of the arrival location algorithm, based on the classical particle swarm optimization algorithm, combined with the fitness function and the method of adaptive changing parameters, the improved particle swarm optimization algorithm can not only effectively solve the problem that particle swarm optimization is sour and easy to fall into local optimization but also accurately locate the position of the passive sound source. The feasibility and stability of the algorithm are verified by actual simulation.
Aerodynamic parameters play a decisive role in the ballistic characteristics of the projectile. How to accurately obtain the aerodynamic parameters of the projectile is an important task in the development process of the projectile. In order to further improve the identification accuracy of the projectile drag coefficient, this paper generates huge ballistic data through numerical simulation and uses the extreme learning method to identify the ballistic drag coefficient under three kinds of noise conditions. The method avoids the iterative updating process of weights and thresholds by randomly generating the input weights and threshold values of hidden layer neurons and overcomes the problem of long identification time of the traditional back propagation (BP) neural network algorithm. Based on the least squares principle, the Moore–Penrose generalized inverse matrix of the hidden layer output matrix was solved to determine the optimal output weight of the network, and then, the projectile drag coefficient was accurately identified. Comparing the extreme learning method with the traditional BP neural network method, the results show that the proposed method has higher identification accuracy and faster convergence speed and can effectively identify the projectile drag coefficient, which can meet the practical needs of engineering.
Concerning the plan trajectory tracking problem of gliding missile, the fast convergence sliding mode control is used in the designing of guidance law. To cope with the problem of parameter perturbation, adaptive method is proposed to update the value of parameter timely. Theoretical analysis and numerical simulation show our method can estimate the angular rate of missile precisely, and performs well in trajectory tracking.
The physical identification of aerodynamic parameters plays a decisive role in studying the characteristics of projectiles. During the development process of projectiles, it is an important task to accurately obtain the aerodynamic parameters of projectiles. In this paper, through a large number of experiments, the flight trajectory data of the projectile are measured, and the maximum likelihood estimation algorithm is used to physically identify the drag coefficient and lift coefficient of the projectile. First, the sensitivity coefficients of each parameter of the projectile trajectory are calculated and deduced. Second, based on the consistency and asymptotic characteristics of the maximum likelihood estimation algorithm, the sensitivity relationship between the velocity and the drag coefficient and between the position of the projectile’s center of mass and the lift coefficient is used to identify the aerodynamic physical parameters of the projectile. The results show that the maximum likelihood estimation algorithm has high identification accuracy, fast calculation speed, and low algorithm complexity, which can effectively identify the aerodynamic physical parameters of the projectile and meet practical engineering needs.
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