In this study, a wearable multichannel human magnetocardiogram (MCG) system based on a spin exchange relaxation-free regime (SERF) magnetometer array is developed. The MCG system consists of a magnetically shielded device, a wearable SERF magnetometer array, and a computer for data acquisition and processing. Multichannel MCG signals from a healthy human are successfully recorded simultaneously. Independent component analysis (ICA) and empirical mode decomposition (EMD) are used to denoise MCG data. MCG imaging is realized to visualize the magnetic and current distribution around the heart. The validity of the MCG signals detected by the system is verified by electrocardiogram (ECG) signals obtained at the same position, and similar features and intervals of cardiac signal waveform appear on both MCG and ECG. Experiments show that our wearable MCG system is reliable for detecting MCG signals and can provide cardiac electromagnetic activity imaging.

Satellite autonomous orbit determination (OD) is a complex process using filtering method to integrate observation and orbit dynamic equations effectively and estimate the position and velocity of a satellite. Therefore, the filtering method plays an important role in autonomous orbit determination accuracy and time consumption. Extended Kalman filter (EKF), unscented Kalman filter (UKF), and unscented particle filter (UPF) are three widely used filtering methods in satellite autonomous OD, owing to the nonlinearity of satellite orbit dynamic model. The performance of the system based on these three methods is analyzed under different conditions. Simulations show that, under the same condition, the UPF provides the highest OD accuracy but requires the highest computation burden. Conclusions drawn by this study are useful in the design and analysis of autonomous orbit determination system of satellites.

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