Multi source asynchronous information fusion technology based on cross prompts and its application

Wei Daozhi,Huang Da,Huang Shucai,X Lei,Y Yu,S Cai,H Li

doi:10.1051/matecconf/201824603021

Wei Daozhi, Huang Da + Show 5 more

Open Access

https://doi.org/10.1051/matecconf/201824603021

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Abstract

Several modern local high-tech wars have shown that the development of war is determined by the precise strike weapons at both ends of attack and defense. In this paper, the urgent requirement of defense ballistic missile target is taken as traction, and the heterogeneous multi-sensor cross-prompt technology is used as support to study the heterogeneous multi-sensor cross-prompt and its application in target detection. On the basis of fully studying the basic theory of heterogeneous multi-sensor cross-prompting, a cross-prompting network structure model based on typical anti-missile combat mission driving and a data fusion model based on asynchronous information are established, and a multi-sensor cross-prompting method based on fuzzy decision is designed. The model and method proposed in this paper are applied to target detection and tracking, and the model and method involved in this paper are verified by simulation.

Highlights

After generalization in nonlinear systems, the basic Kalman filtering becomes Unscented Kalman filtering
Compared with the basic Kalman filtering algorithm, the adaptive filtering algorithm has obvious advantages. It can adjust the prediction residual, which contains the correction of the unmeasured information, so it can effectively deal with the impact of abnormal prediction information on state estimation, and can minimize the error
The estimation of useful parameters can be obtained, which can minimize the error in the case of observation anomalies and obtain better state estimation

Summary

Introduction

After generalization in nonlinear systems, the basic Kalman filtering becomes Unscented Kalman filtering. Compared with the basic Kalman filtering algorithm, the adaptive filtering algorithm has obvious advantages It can adjust the prediction residual, which contains the correction of the unmeasured information, so it can effectively deal with the impact of abnormal prediction information on state estimation, and can minimize the error. The error discriminant statistics and adaptive factors of the state model are constructed with predictive residual as variables [4,5]. The received observation signals contain a variety of signals, in addition to the required useful signals, there are unwanted signals, such as random observation noise and interference signals For this reason, when processing the actual observation data, it is necessary to deal with the observation noise and interference signal, and do robust estimation. (1) Under the assumptions of the designed model, validity is the basic requirement for parameter estimation, in other words, the estimated value is in or near the optimal state. The robust estimation is suitable for the state estimation problem when the system model is inaccurate

M estimation principle of robust estimation

Method