Recognition model and algorithm of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain

Abstract

In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system, this work proposes a new recognition method of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain of six sky-screens detection sensor. Based on the measurement principle of the six sky-screens intersection test system and the characteristics of the output signal of the sky-screen, we analyze the existing problems regarding the recognition of projectiles. In order to optimize the projectile recognition effect, we use the support vector machine and basic particle swarm algorithm to form a new recognition algorithm. We set up the particle swarm algorithm optimization support vector projectile information recognition model that conforms to the six sky-screens intersection test system. We also construct a spatial-temporal constrain matching model based on the spatial geometric relationship of six sky-screen intersection, and form a new projectile signal recognition algorithm with six sky-screens spatial-temporal information constraints under the signal classification mechanism of particle swarm optimization algorithm support vector machine. Based on experiments, we obtain the optimal penalty and kernel function radius parameters in the PSO-SVM algorithm; we adjust the parameters of the support vector machine model, train the test signal data of every sky-screen, and gain the projectile signal classification results. Afterwards, according to the signal classification results, we calculate the coordinate parameters of the real projectile by using the spatial-temporal constrain of six sky-screens detection sensor, which verifies the feasibility of the proposed algorithm.