An Automatic Digital Camouflage Pattern Generation Method based on Texture Structure

Abstract

This article investigates the problem of scattered burst signal detection based on multiple sensors to obtain overall decisions. In the explosion detection system studied in the article, sensors independently transmit their decisions on measuring explosion information to the data fusion processing terminal, which provides overall decisions based on fusion rules. The researchers focus on the data fusion theory of the distributed parallel detection burst point data fusion system based on the Bayesian rule. This paper has obtained the data fusion rule and sensor decision criteria that make the overall system optimal, and proposed a nonlinear Gauss Seidel mathematical variable algorithm that optimizes the data fusion rule and multi-sensor decision criteria The data fusion problem when detecting burst point signals with two different and three identical types of sensors. The data fusion algorithm proposed in this article is validated and simulated through computer experiments on the detection of three types of sensors. The relevant experimental data show that the performance of a data fusion system based on Bayesian detection is significantly improved compared with the sensor acquisition of burst point information. In the experiment, the risk of Bayesian missing detection of burst point signal coefficient of the data fusion system using three sensors with the same performance is reduced by 32.7%.