Research On Weak Magnetic Sensor Based On Magnetoelectric Composites

The Russian-Ukrainian conflict that broke out in 2022 shows that modern military operations are no longer limited to traditional land,naval and air warfare,but also include new battlefields such as electronic warfare,public opinion warfare,and information warfare.Since the 21 st century,the technical level of information warfare has developed rapidly and fiercely.Therefore,to fight information warfare well,it is necessary to master the country's information security in all aspects.The marine magnetic field information is an indispensable part of the construction of the marine comprehensive protection system.With the acceleration of the development and development of the marine economy,the deepening of oceanographic research and the urgent needs of marine defense construction,the marine weak magnetic detection and measurement technology will play an increasingly important role in military applications and marine economic construction.By detecting the magnetic anomaly signal near the target,the magnetic characteristic signal of the magnetic anomaly target can be identified,and the motion state of the magnetic anomaly target can be inverted by using these characteristics,such as position,linear velocity,angular velocity,acceleration,etc.Fast positioning and tracking of targets.In this way,early warning,identification,monitoring,and tracking of unknown underwater targets can be realized,the protection of ports and wharves near the coast,and the monitoring of important ocean passages,etc.,for my country's defense of territorial waters,maintenance of national sea control,and construction of the world's maritime power in the twenty-first century has a significant role in actual combat.This paper mainly focuses on the application of magnetoelectric sensors based on magnetoelectric composite materials in the detection of magnetic anomalies.Starting from the characteristics of the sensor,a magnetic anomaly characteristic signal recognition algorithm that can be applied to the noise characteristics of the sensor is studied,as well as a tracking algorithm for magnetic anomaly targets.From the perspective of magnetic sensors,the basic sensitive principle of magnetoelectric sensors based on magnetoelectric composites is aimed.The magnetoelectric response coefficient model of the magnetoelectric composite material under different electrical boundary conditions is established,and the relationship between the magnetoelectric response coefficient and the material's intrinsic parameters is studied.The intrinsic noise model of the magnetoelectric composite material was established to evaluate the detection limit of the magnetoelectric composite material.The responsivity model of the magnetoelectric sensor connected to the preamplifier circuit is used to analyze the cause of circuit imbalance at zero input and point out the solution.Establish the signal transmission and noise coupling relationship between the material and the circuit of the sensor.According to the characteristics of the magnetic anomaly signal,the blind source extraction algorithm is optimized.Compared with the traditional EMD decomposition method,the residual error of the CEEMDAN decomposition method can detect the magnetic anomaly signal under the low signal-to-noise ratio.the reason for the peak.The low-pass filtering of the eigenmode components is achieved by the method of wavelet threshold;the high-pass filtering of the eigenmode components is performed by the method of the lowest average energy.The results show that when using the CEEMDAN modal decomposition method to detect magnetic anomalies with low signal-to-noise ratio,the peak error rate is 0.5% at 4d B,and the error rate is less than15% at-20 d B.Taking the implementation position and inherent magnetic moment of the magnetic anomaly target as state variables,under the framework of the extended Kalman filter,the extended Kalman filter is an extended Kalman filter that combines the state equation of the prior knowledge of time and distance with the observation results of the sensor.The motion state of the magnetic anomaly target is estimated recursively,and the extended Kalman filter with first-order precision realizes the state estimation under the ultra-low signal-to-noise ratio of-40 d B.According to the analysis of the state estimation results of EKF,the error of the estimated trajectory of the EKF algorithm relative to the real trajectory is 16 m at most,while the closest distance between the sensor and the trajectory is 72 m,and the maximum error rate is22.22%.When the magnetic anomaly target gradually moves away from the magnetic sensor,the change rate of the magnetic signal gradually slows down,and the estimated deviation of the EKF gradually converges to less than 3 m,and the corresponding error rate converges to less than 3%.