To evaluate the protection performance of SPD (surge protective device) against electromagnetic pulse, the response ability of several typical surge protective devices to wide and narrow electromagnetic pulses was tested by using a SPD response ability test system. The results showed that SPD commonly used in lightning surge protection had certain ability to suppress electromagnetic pulse conduction disturbance. Gas discharge tubes presented typical clamping characteristics for wide pulses. MOV and TVS had obvious clamping effect on wide pulses, while had no clamping effect on narrow pulses, but could obviously reduce its peak value. Zener diodes had obvious clamping effect on narrow pulses, and the clamping voltage control accuracy was high.

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In order to analyze the lightning coupling of the airborne secondary radar electronic equipment, this study analyzes the transient electromagnetic field strength of the airborne secondary radar electronic equipment and the current density distribution on the aircraft surface when the lightning directly hits the aircraft based on the simulation technology of the transmission-line matrix method. On this basis, the simulation research of field distribution and cable coupling is further carried out. Through the simulation calculation, it can be seen that when the lightning strikes the aircraft, the current density is relatively small in the continuous parts of the upper and lower surfaces of the fuselage and the middle of the wing. In addition to the areas where the lightning current is easily attached, structural irregularities such as windows, engines, front and rear edges of wings, and outboard antennas are often affected by edge effects, and the current density is often concentrated, requiring key protection. For more sensitive electronic equipment, the cable connected to it can be considered to use an appropriate type of shielded cable to achieve the shielding effect on the electromagnetic environment.

In terms of driverless systems, high-precision positioning technology is one among the critical aspects of driverless cars to achieve driverlessness. This study analyzed the working principles of GNSS (global navigation satellite system) and SINS (strapdown inertial navigation system) and elaborated the principles of the least square method and LAMBDA algorithm in the integer ambiguity resolution. Based on the network RTK positioning technology and the abovementioned theory, the unmanned automatic work vehicle was used as the research object, and the fusion positioning algorithm of the BDS/GPS system and inertial sensor was used to propose a high-precision positioning technology for the unmanned automatic work vehicle. The combined navigation system model was studied and constructed. Relevant verification was carried out through simulation and experiment. The results were as follows: the pitch angle error was less than 0.1°, the roll angle error was less than 0.05°, the speed error was less than 0.2 m/s, and the position error was less than 2.1 m. The outcomes indicate that an integrated navigation and positioning algorithm for driverless vehicles can significantly enhance the localisation accuracy and reliability of navigation. The research results are of engineering value and practical application for the development of unmanned automatic special vehicle positioning systems.

Machine learning is a key problem in the field of artificial intelligence. It is the study of statistical learning methods. It enables computers to simulate the learning behavior of humans, accumulate experience, and continuously improve and perfect their performances. Mechanical motion has always been an important subject in the field of automation, and trajectory tracking control is an important technology for mobile robots. Therefore, its research has important theoretical and practical significance. This study proposes a research based on the machine learning algorithm applied to the control and tracking system of mechanical motion trajectory. It expounds the neural network model, support vector machine algorithm, clustering algorithm, and K-means algorithm. Zeroing in on the issue of mechanical movement direction following control, this study concentrates on the plan of the mechanical movement direction following regulator in view of the BP brain network in the AI calculation and checks the accuracy and plausibility of the regulator plan. The exploratory outcomes demonstrate the way that the ideal following direction can be acquired by taking ε = 5. It makes the tracking trajectory more accurate, and the error convergence speed is faster.