Objective To ensure the stability in-flight operation of airborne opto-electronics equipment, it is necessary to efficiently test its functions, performance, and stability under a simulated environment within a ground maintenance situation. Current researches in this field primarily focus on human-machine interface software testing, where the testing and simulation instruments are separate. It emphasizes optical performance, lacks of integrated method, and lacks of testing methods for specialized equipment’s electronic parameters, resulting in low testing and simulation efficiency. To address these issues, this paper proposes a dual-fusion method for airborne opto-electronics equipment testing and simulation driven by trajectory points.

Methods This method integrated manual static testing and automatic dynamic simulation into a unified framework, focusing on the core parameter testing of opto-electronics equipment such as line-of-sight pointing angle. A schematic deployment and an overall detailed framework for the dual-fusion method were designed, along with a temporal framework and a software framework for engineering implementation. To address several key issues within the dual fusion method—namely, how to plan trajectory points with start/end points and extreme values given, when to anto-start automatic simulation commands, and how to achieve high-precision verification of line-of-sight (LOS) pointing—corresponding mathematical models were established. Using linear increment under level flight conditions as the base model, a sinusoidal variation model for attitude angles and an incremental model for coordinated turn conditions were incorporated. Based on the route start/end points, angle extreme values, and turn radius, a trajectory point simulation data generation model was established. This model generated position and attitude data for each trajectory point at a fixed sampling interval. Based on calculating the distance from a point to a fixed straight-line path at the same altitude, and considering that using a fixed distance threshold to determine the auto-start point might lead to auto-start failure, the key trajectory point command auto-start decision model was established. The theoretical foundation of this model was the parabolic variation of the point-to-line distance (i.e., approaching first, then receding, and approaching again). By calculating a distance sequence and identifying the minimum distance point, the model determined when and where to auto-start the simulation command. Using a LOS pointing calculation method which based on two points’ position (i.e., longitude, latitude, altitude) and attitude (i.e., heading angle, pitch angle, rolling angle) as the base model, and considering that communication delays may cause theoretical values different from the real-time feedback values by equipment, then lead to false out-of-tolerance errors, the equipment status consistency verification model was established. The theoretical foundation of this model involved utilizing delay data measured by an oscilloscope to eliminate the lead of the theoretical calculation unit, aligning the clock baseline of the theoretical calculation unit with that of the equipment communication unit, thereby reducing the probability of false alarms. The aforementioned models respectively accomplished the generation of simulation data, the starting of simulation commands, and the verification of simulation states, forming a complete testing and simulation workflow. For parameters in the models such as point selection, delay prediction, and delay elimination, explanations or rules for parameter selection were provided. Regarding the assumptions made in the models—such as constant altitude, disregarding elevation, and assuming acceptable delay prediction errors—a discussion of limitations was conducted.

Results and Discussions The dual-fusion method was experimentally validated. First, functional testing was performed on the trajectory point simulation data generation model. Then, the key trajectory point command auto-start decision model was experimentally validated. Experiments were conducted between the model used a fixed threshold and the model established in this study under different startup distances. Finally, the equipment status consistency verification model was experimentally validated. The experiments compared the baseline model without delay-elimination with the model established in this study, evaluating the equipment status false alarm rate under level flight and coordinated turn conditions at different simulation velocity-height ratios. The experimental results demonstrate that: 1) the trajectory point simulation data generation model functions correctly, and the data generated by the model aligns with the model's calculations; 2) compared with the fixed threshold model, the key trajectory point command auto-start decision model could auto-start simulation commands at any shortest distance, reducing the probability of auto-start failure to 0%; 3) compared with the model free from delay-elimination, the false alarm probability of the equipment status consistency verification model is significantly reduced; The maximum false alarm probability decreased from 45% to 3%, a reduction of 20%. In the discussion of the experimental results, the applicable conditions and scope for the method's effectiveness were discussed. The method achieves both testing and simulation functions, with seamless switching between the two, demonstrating excellent integration.

Conclusions Experiments confirm that, through framework and model design, this method resolves the separation between testing and simulation methods. Furthermore, the simulation method focuses on core parameters unique to opto-electronics equipment, such as the LOS pointing angle. This research bridges the gaps in the existing framework regarding automatic simulation and testing/simulation integration. The findings hold significant reference value for the technological advancement and engineering implementation in the field of ground testing for airborne opto-electronics equipment.