Research on Aeromagnetic Compensation Method Based on BP Neural Network
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摘要:
水中未爆弹危害极大，航空磁探因探测效率高的优势，常用于水中未爆弹的探测。飞机干扰磁场的存在限制了航空磁探的发展。传统的 T-L 模型待求参数间具有很强的复共线性，难以满足高精度磁补偿的要求，而神经网络算法具有容错率高、求解精度高的特点。首先使用 BP 神经网络建立干扰磁场的数学模型； 随后，通过仿真生成干扰磁场以及未爆弹目标磁场信号对算法进行验证；最后，利用四旋翼无人机平台进行目标探测试验，补偿改善比超过 20。试验结果表明，算法可以用于提高平台对水下目标探测的精度。
Abstract:
Underwater unexploded bombs are extremely harmful，and aerial magnetic detection is often used for underwater unexploded bombs due to its high detection efficiency. The existence of interfering magnetic field of an airplane limits the development of aeromagnetic detection. The traditional T-L model has strong complex collinearity between the parameters to be solved，which is difficult to meet the requirements of high-precision magnetic compensation. Neural network algorithm has the characteristics of high error tolerance rate and high solution accuracy. In this paper，the BP neural network is used to establish a mathematical model of the interfering magnetic field. Subsequently，the algorithm is verified by generating the interference magnetic field and the magnetic field signal of the unexploded bomb target by simulation. Finally，the target detection test is carried out using the quadrotor UAV platform，and the compensation improvement ratio exceeds 20. The test results indicate that the algorithm can be used to improve the accuracy of underwater target detection on the platform.
