Based on polyimide (PI), epoxy resin (EP), polytetrafluoroethylene (PTFE) typical dielectric materials used in spacecraft, a research platform for charge-discharge experiment under strong field was established. The influence of irradiation field strength, beam energy, dielectric thickness, etc., on electrostatic discharge characters of dielectric materials were studied and summarized. The results show that with the increase of field strength, the frequency of induced electrostatic discharge increases, the accumulated charge on the dielectric surface decreases, and the dielectric surface potential decreases; Under the same conditions, PI has the lowest discharge frequency and the highest surface dynamic balance potential. The thicker the dielectric material, the lower the discharge frequency, the higher the surface dynamic equilibrium potential; The shape and size of the background electrodes also affect the discharge frequency. When the area is the same, the sharper the electrode edge, the higher the discharge frequency. When the shape is the same, the larger the grounding area, the higher the discharge frequency. By proposing the induced discharge test method, the function mechanism of spatial environmental factors on the electrostatic discharge of typical dielectric materials are obtained. Comparative analysis on electrostatic properties of different dielectric materials can provide data reference and technical support for spacecraft electrostatic safety and protection.

The indoor localization of people is the key to realizing “smart city” applications, such as smart homes, elderly care, and an energy-saving grid. The localization method based on electrostatic information is a passive label-free localization technique with a better balance of localization accuracy, system power consumption, privacy protection, and environmental friendliness. However, the physical information of each actual application scenario is different, resulting in the transfer function from the human electrostatic potential to the sensor signal not being unique, thus limiting the generality of this method. Therefore, this study proposed an indoor localization method based on on-site measured electrostatic signals and symbolic regression machine learning algorithms. A remote, non-contact human electrostatic potential sensor was designed and implemented, and a prototype test system was built. Indoor localization of moving people was achieved in a 5 m × 5 m space with an 80% positioning accuracy and a median error absolute value range of 0.4–0.6 m. This method achieved on-site calibration without requiring physical information about the actual scene. It has the advantages of low computational complexity and only a small amount of training data is required.

Characterized by noninvasiveness and high sensitivity, the noncontact electrostatic testing technology has been widely used in scientific research and engineering practices such as electrostatic charge imaging, high-voltage insulator analysis, biological sensing, and gas–solid two-phase flow parameter measurement. However, in many practical applications, scanning electric potential microscope technologies focusing on high spatial resolution cannot meet the testing requirements of charged objects with irregular 3D structures and flexible activities. Based on the principle of direct inductive electrostatic potential measurement and a six-axis robot arm, a three-dimensional scanning method for noncontact electrostatic potential measurement is proposed in this paper. A low-noise electrostatic potential sensor with both high sensitivity and high accuracy was designed via positive feedback circuit technology. A calibration system was built, which has an adjustable excitation signal, progressive spatial accuracy, and variable 3D attitude. The experimental data verify the effectiveness of this method. The correlation between the signal characteristics and the physical size of the calibration device was statistically analyzed. The effects of parameters such as the amplitude and frequency of the excitation signal, speed, and normal distance of the robot arm scanning on the resolution of the test results were investigated. The testing results show that the 3D spatial resolution of DC and low-frequency AC potential distribution reached 200 μm.

Autonomous platforms have been widely adopted in both civilian and military contexts, achieving notable success. However, their energy resources, computational power, payload capacity, and cost are constrained and interdependent. These limitations prevent the integration of advanced threat detection systems, such as high-speed cameras and radar. Consequently, they are unable to detect high-velocity projectiles, significantly undermining their survivability in hostile environments. Developing low-power, cost-effective, and computationally efficient threat detection methods is of critical importance. This letter proposes a method for perceiving the flight trajectories of charged objects based on the principle of electrostatic induction. A physical model for electrostatic detection is established, and the effects of flight speed and trajectory on induction signals are analyzed. Inspired by sharks, a four-element array testing system with specific orientation and spatial distribution characteristics is designed. An indoor experimental setup is developed to simulate and test the flight of charged objects, generating a comprehensive dataset by varying parameters such as flight speed, incident angle, and testing distance. This dataset is then used to train a symbolic regression machine learning model, resulting in a mathematical model capable of predicting the incoming direction of charged objects with an error margin of less than 10 degrees. The model's generalization ability and the impact of the number of sensors are also discussed.

Here, a new analytical method, state matrix transform method, is proposed for calculating the descriptions of current waveforms generated by commercial electrostatic discharge (ESD) generators. This method can be used to analyse and design lumped parameter ESD circuits. A relatively simple eight elements ESD circuit model is established and used to predict the current waveform of human‐metal ESD events. To calibrate the ESD simulator, a mathematical expression of four exponentials curve for ESD current waveform is given. This careful theoretical study of ESD current waveform calculation makes an innovative contribution to ESD generator circuit simulation and design, and it provides a new mathematical description for ESD sensitivity test.

Noncontact surface potential measurement approaches with high spatiotemporal resolution are suitable for insulating and conducting materials and have the advantages of noninvasiveness and high sensitivity. These methods show promising application prospects in fields, such as material surface charge imaging. However, reduced electrode size leads to issues such as induction signal attenuation and interference between electrode arrays, limiting the improvement in the spatial resolution and test accuracy. Inspired by the visual principle of lateral inhibition, we propose a biomimetic measurement method that addresses these issues by utilizing the weighted summation of multichannel induction signals and lateral inhibition spatial functions. A lateral inhibition biomimetic circuit is designed, and a circuit parameter calculation method is established. A biomimetic sensor is implemented, and a prototype experimental system is constructed. The proposed method is experimentally validated using test samples featuring line and dot matrix pattern potential distributions. The results indicate that appropriately selected lateral inhibition weights can effectively improve surface potential imaging quality. Optimal weights can increase the structural similarity between the test and simulation results from 0.71 to 0.83 and the peak signal-to-noise ratio from 23.63 to 25.70 dB. The Gershon lateral inhibition function demonstrates higher validity and generality than other inhibition functions.