In recent years， unmanned aerial vehicles have become an indispensable and important weapon in modern warfare， and the offensive and defensive confrontation system covering unmanned aerial vehicle systems has become one of the basic characteristics of modern warfare. Studying the survivability problem of unmanned aerial vehicle systems under strong denial conditions has important application value for future battlefield confrontation. This paper analyzed the challenges faced by combat unmanned aerial vehicle systems in the physical， electromagnetic， and information domains and gave targeted strategies to cope with different denial conditions. The combat scene， combat configuration， and movement tactics were proposed for unmanned aerial vehicle systems under strong denial conditions. The key problems related to survivability were studied， including the bifocus random walk algorithm of relay swarm， the secondary planning technology of denial space path， and the service-oriented architecture technology of intelligent edge accusation center. By optimizing the overall design of the combat system， the survivability of unmanned aerial vehicle systems under strong denial conditions can be effectively improved to cope with the complex combat environment of future battlefields.

To address the significant deviations between actual and theoretical aiming error values of the radar radome， which are difficult to compensate for precisely using ground data， this paper proposed a multi-model filtering algorithm. Considering the distribution differences of aiming error due to line-of-sight angle dispersion of the seeker body， the paper analyzed the characteristics of low-speed ground motion targets and employed an interactive multi-model approach combined with the measurement-augmented unscented Kalman filter algorithm to improve the accuracy of aiming error estimation. During the terminal guidance phase， the line-of-sight angle of the seeker body gradually decreased. This method processed large-angle data to identify target motion and then small-angle data to estimate aiming error in real time. Simulation results indicate that the proposed algorithm is an effective approach for estimating aiming error under equality constraints， significantly improving estimation accuracy compared to traditional methods.

A maximum correlation entropy Kalman filter （MCKF） error correction method is proposed to address the issue of test data errors caused by errors in the testing equipment itself and improper operation by testing personnel during the testing process of various equipment performance parameters. This method improves the accuracy of error correction by introducing maximum correlation entropy to process non Gaussian noise interference in test data. Applying it to the error correction of performance parameter test data of a certain device， the simulation results show that this method can effectively handle the impact of non Gaussian noise interference on the error correction accuracy in the test data.