Integrated infrared and radar stealth optimization for the exhaust system of UCAV using multi-fidelity data fusion and Bayesian optimization

The resource-intensive, high-fidelity infrared signature simulations and Radar Cross-Section (RCS) calculations limit the integrated optimization of Unmanned Combat Aerial Vehicles (UCAVs) in response to escalating threats from joint detection systems. To this end, we present a sample-efficient framework to advance the optimization efficiency of UCAV’s exhaust system, focusing on both the stealth characteristics evaluation and the optimization process. A novel multi-fidelity stealth assessment method, powered by multi-fidelity neural network and local perceptive fields, has been developed to fuse different fidelity information from infrared radiation signature and RCS values, respectively. Results demonstrate that the method can achieve relatively high accuracy based on a small set of high-fidelity data. Furthermore, this data fusion method is integrated into a multi-objective Bayesian optimization framework. Employing a Gaussian process regression model and the EHVI acquisition function, the framework effectively explores the stealth objective space, achieving a 15.21% hypervolume indicator increase with fewer optimization iterations compared to NSGA-II. Results show that the optimized nozzle significantly reduces both the infrared signature and RCS compared to the baseline configuration. The proposed framework offers a practical and efficient approach for optimizing the integrated stealth performance of UCAVs.