Research On Bomb-borne Multiplexed Magnetic Signal Fusion Processing Technology Based On Kalman Filtering

With the continuous adjustment of military strategy,the measurement of muzzle magnetic field has gradually entered everyone’s field of vision.In recent years,researchers in related fields have begun to calculate the projectile speed by measuring the muzzle magnetic field data.In this process,the accuracy of the muzzle magnetic field data will directly affect the results of the projectile attitude calculation.However,the muzzle magnetic field environment is extremely complex,and there are also errors in sensor measurement.Therefore,processing the muzzle magnetic field data is particularly important.This article proposes an adaptive fusion filtering algorithm that utilizes multi-sensor array measurement and Extended Kalman Filter(EKF)for the fusion of multiple magnetic signals on board missiles.The basic idea is to establish a sine function model of the muzzle magnetic field by analyzing the changes in the magnetic field at the muzzle,and use this model to establish a Kalman based prediction model to fuse and filter the muzzle magnetic field signal.The main work of this article includes:(1)Analyzed the multi-sensor array measurement model and the variation law of the magnetic field at the muzzle,established a sine function model of the muzzle magnetic field over time based on existing experience,and improved it through Gaussian Newton parameter identification method.(2)Designed a multi-sensor fusion algorithm based on Kalman filtering;This algorithm first establishes a Kalman based prediction model based on the sinusoidal variation model of the muzzle magnetic field,completes the Kalman prediction,and then uses an algorithm based on fusion closeness to perform data fusion calibration and complete the Kalman update.(3)Improving on the EKF multi-sensor fusion algorithm;In response to the low adaptability of Gaussian Newton method for parameter identification,a neural network with strong learning ability and adaptability is used to improve the model,thereby reducing the error of the muzzle magnetic field model,improving the accuracy of the EKF prediction model,and obtaining better filtering effects.(4)Combined with the simulation experimental data to simulate the neural network EKF adaptive fusion algorithm proposed in this paper,the results show that the filtering effect is better,and the accuracy of the muzzle magnetic field data is improved by 9.5%.