Moving Target Depth Information Extraction Based on Nonlinear Strategy Network

Abstract

To improve the effect of depth information extraction of moving targets in the network, a nonlinear strategy-oriented method is proposed. With the advancement of science and technology, especially in wireless networks, a large amount of data is provided to people every hour of every day. Hence, it can increase the demand for data analysis tools. Nonlinear system modeling by using rough set theory to extract valuable information from large amounts of information, and then through the analytic hierarchy process (ahp) to determine the effect of input factors, then use particle swarm optimization algorithm (PSO) to find the accurate function, and USES the adaptive and population catastrophe and vaccine algorithm to make it to the local optimum, to achieve the aim of the complex. The experimental results show that, compared with M2 and M1 for 30 groups of samples, the model obtained by using M2 has a better fitting effect on the actual curve. The error of M2 is within ±3%, and the error of M1 is within ±6%, and the error is relatively large. The accuracy of the proposed method is higher than that of the neural network method, which proves that the nonlinear strategy is effective in the actual target depth information extraction.