Browse Topic: Management and Organizations
To address the low computational efficiency problem by numerical calculation and large cumulative error by long-term sequential iterative prediction during the process of predicting the satellite’s transient temperature fields, this paper proposes a Physics-Informed U-Net (PI-Unet) model. Specifically, this PI-Unet model uses multi-branch spatiotemporal features as its inputs and combines hard constraints with a physics-informed loss to ensure the physical consistency of the predicted temperature field. Furthermore, for the error accumulation problem in long-sequence autoregressive prediction models, this paper presents a model training method based on the concept of filtering. Under the guidance of this method, the PI-Unet model can develop a long-term sequence prediction capability while keeping the accuracy of spatial temperature field prediction, achieving high-precision spatiotemporal prediction. By actively injecting prediction errors through a scheduled sampling strategy, along with a multi-step rolling training strategy, the PI-Unet model not only learns the governing physical laws of the spatial distribution of the transient temperature field, but also acquires the filtering capability to mitigate the accumulation of the long-term sequence rolling prediction errors. The experimental results indicate that the PI-Unet model maintains the maximum absolute error of predicting spatial transient temperature field stably below 5.5K and the error of the rolling-predicted transient temperature field in one orbit period less than 6K. In addition, the time for a single-step prediction is reduced to 0.005s, and the time for a total-period prediction is less than 0.5s. Therefore, this research provides a strong and high-efficiency technical support for the real-time monitoring of the satellite’s on-orbit temperature fields.
The demand for high-precision, real-time, and continuous localization in aerospace and low-altitude unmanned systems is steadily increasing. However, under weak GNSS availability, obstruction, and degraded ranging geometry, conventional 3D localization methods often suffer from insufficient observation constraints and severe accuracy degradation. To address observability degradation and localization error divergence in single-auxiliary-node systems, this paper proposes a 3D cooperative localization method based on maneuver reconfiguration of a single auxiliary node. Within a particle filtering framework, the method fuses target motion increments with single-shot range measurements to recursively estimate the target’s 3D position and heading. A geometric degradation metric and a radial-consistency metric are further constructed from the line-of-sight distribution and the alignment between target velocity and line of sight within a sliding window, enabling online detection of degraded observability. Once degradation is detected, the auxiliary node performs either lateral maneuvering or circular maneuvering to actively reshape the target–auxiliary relative geometry and restore observation diversity; meanwhile, the two metrics can also serve as feedback variables for adaptive maneuver adjustment. Comparative analyses are conducted under three modes, namely no reconfiguration, lateral maneuvering, and circular maneuvering, from the perspectives of 3D trajectory evolution, error time series, degradation-metric time series, and statistical error performance. Parameter sensitivity analyses and an additional nonlinear velocity-varying scenario are also included. Experimental results show that the no-reconfiguration mode exhibits clear error divergence under degraded conditions, whereas both maneuver-based strategies effectively reduce localization errors. Lateral maneuvering is more suitable for rapid recovery in the early degradation stage, while circular maneuvering achieves better overall accuracy and stronger post-degradation recovery capability. These results demonstrate the effectiveness and engineering feasibility of degradation-triggered active geometric reconfiguration for cooperative localization in low-configuration unmanned systems.
In recent years, the global demand for high-rate, high-reliability, and wide-coverage wireless communications has continued to grow. Traditional satellite communications are faced with bottlenecks such as spectrum scarcity, insufficient single-satellite capacity, and poor adaptability to highly dynamic channels. Relying on the advantages of wide-area coverage and multi-satellite cooperation, low Earth orbit (LEO) satellite constellations can construct distributed satellite MIMO systems and form virtual large-scale antenna arrays, significantly improving system capacity and reliability. However, such systems are characterized by large spatiotemporal scales, and their channels exhibit strong heterogeneity in dimensions such as delay, frequency, and polarization, which are difficult to accurately evaluate using existing methods. To this end, this paper proposes a large-spatiotemporal-scale channel characteristic analysis method for distributed LEO satellite MIMO systems. A satellite-terrestrial full-link channel model is established, and quantitative characterization of multi-dimensional heterogeneity is realized based on statistical boundary analysis. Combined with the H3 global grid system, large-scale simulations are carried out to statistically analyze the distribution laws of delay differences, frequency offsets, and polarization angle deviations under different user configurations. The unique value of this analysis method lies in that its output results can be used to determine the upper limit of buffer capacity required for the receiver to complete coherent combining of multi-channel signals and the frequency offset correction range required for signal acquisition, provide a quantitative basis for optimizing satellite set selection, and offer a solid theoretical model and data support for performance evaluation and parameter design of multi-satellite cooperative systems.
Modern space exploration relies more and more on vehicles that carry expansive antenna arrays, a trend driven by steady progress in the field of space engineering. The study centers on constructing a coupled model that accounts for structural rigidity, flexibility, and thermal effects in a sizable orbital unfurling parabolic antenna, along with designing its orientation regulation system. A blended coordinate formulation and a heat-based discretization approach are employed to derive the motion and temperature-governing equations of the assembly. A unified pointing control method is then developed by merging classical feedback control principles with torque compensation control. Computational experiments involving a spacecraft fitted with such a dish antenna are executed. The findings reveal that sudden heat fluctuations, exemplified by the transition out of Earth’s umbra, trigger oscillatory behavior in the antenna framework and disrupt the satellite’s angular position. The designed orientation regulator successfully counteracts these thermally driven perturbations, even when the system representation contains inaccuracies.
Real-time monitoring of metal wear debris in lubricating oil is crucial for accurately assessing aerospace equipment health and preventing catastrophic mechanical failures. Although three-coil differential inductive sensors are widely used due to their high sensitivity and robust structure, their detection performance inevitably degrades under the influence of multi-physics coupling effects inherent in complex industrial environments. Existing analytical models were primarily limited to single electromagnetic field simulations, failing to capture the intricate electromagnetic-thermal-mechanical interactions, which reduced analytical accuracy and hindered the optimization of sensor designs for extreme conditions. To overcome these challenges, this study established a comprehensive parametric simulation model to conduct a rigorous electromagnetic-thermal-mechanical coupled analysis of the differential sensor. Specifically, the research analyzed the underlying mechanisms by which wide temperature variations from -55 °C to 200 °C and specific debris kinematics exert varying degrees of influence on the induced voltage. Quantitative results indicated that both the isolated thermal and mechanical fields altered the voltage by a maximum of 0.649%, demonstrating that the sensor maintains a relatively stable response across extreme thermal gradients. Furthermore, the analysis revealed that increased debris velocity decreased the induced voltage amplitude due to the intensified motion-induced eddy current effects. Notably, in complex multiple debris scenarios, the strong dominant signals generated by ferromagnetic wear debris severely masked the weaker responses from non-ferromagnetic ones, indicating a substantial missed detection risk under mixed contamination conditions. Ultimately, this research successfully filled the critical simulation gap for evaluating these sensors under multi-physics coupling, providing robust theoretical guidance for their reliable deployment in complex industrial applications.
Defense acquisition often struggles to match the pace of private investment, slowing the transition of mature commercial technologies into military use. This paper examines how aligning government acquisition with venture-oriented business models can increase industry participation, accelerate fielding, and reduce government program office risk. Using autonomous construction as a case study, it highlights how commercial investment has advanced autonomy while traditional procurement limits adoption. The paper outlines approaches such as non-traditional partnerships, phased acquisition, and performance-linked revenue structures to improve flexibility, leverage private capital, and expand the Defense Industrial Base while speeding operational capability delivery. Citation: Mazzara, M., San Nicolas, A., Gadea, J., Himmel, M., Kruger, J., Gill, C., & Simon, A., Soylemezoglu, A., Netchaev, A., Nottage, D., Klein, J. “Mobilizing Innovation: Venture Capital Alignment for Defense with Autonomous Construction Case Study” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA Michigan Chapter, Novi, MI, August 11–13, 2026.
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