| When detecting distant ferromagnetic objects,the intensity of the magnetic anomaly signal generated by the ferromagnetic target is often much lower than that of the geomagnetic field.Magnetic anomaly signals are submerged by background noise,resulting in low signal-to-noise ratio and difficulty in detecting magnetic anomalies.This thesis analyzes the characteristics of magnetic anomaly signals,studies signal denoising methods and error calibration methods for the signals output by the magnetometers.The main work is as follows:Analyzes the causes of magnetic anomalies and the main interferences in magnetic anomaly signals,establishes an equivalent magnetic dipole model for ferromagnetic targets,and analyzes the time-domain and frequency-domain characteristics of magnetic anomaly signals through simulation signals,providing theoretical support for subsequent denoising methods.The temporal coherence of magnetic anomaly signal and geomagnetic background signal is studied,and a noise suppression algorithm based on temporal coherence is proposed.The algorithm uses coherence to calculate the time transfer function of the background noise and estimate the noise in the magnetic anomaly signal.Combining the noise suppression algorithm with Orthogonal Basis Function(OBF)algorithm for magnetic anomaly detection can improve the accuracy of detection effectively.Compared with traditional adaptive coherent noise suppression methods,this method requires fewer sensors to collect signals,greatly reducing the cost of magnetic anomaly detection.The testing of simulated and measured signals shows that the proposed method can improve the signal-to-noise ratio of magnetic anomaly signals by approximately 7.38 to 15.17 dB.A denoising method of magnetic gradient signal is studied and proposed,which uses wavelet packet decomposition and reconstruction method to filter high-frequency noise,then uses information entropy to build a denoiser,and applies the denoiser to the gradient OBF energy of the signal to detect magnetic anomalies.This method can reduce the probability of misjudgment in gradient OBF methods and has higher detection accuracy than traditional gradient OBF methods.The testing of simulated and measured signals shows that the proposed gradient signal denoising method can improve the signal-to-noise ratio by approximately 4.38 to 12.74 dB.The error sources of the magnetic signal were analyzed,and the error models of single magnetometer and the error model between the two magnetometers were established.A magnetometers error calibration algorithm was proposed,and the invasive weed optimization algorithm was used to solve the model parameters.Achieving the calibration of nonorthogonal errors,sensitivity inconsistency errors,zero shift errors,and misalignment errors.The test results of simulated signals indicate that the correction algorithm can effectively improve the accuracy of magnetic anomaly gradient signals.Compared with traditional correction methods,the correction algorithm proposed in this paper has better correction performance.The test result of simulated signals indicates that the calibration algorithm can improve the accuracy of magnetic anomaly gradient signals effectively.Compared with the traditional calibration methods,the calibration algorithm proposed in this thesis has better calibration performance.This thesis also uses Visual C++ to write a software for collecting and analyzing magnetic signals,achieving the collection and rapid analysis of multi-sensor magnetic signal data. |
