The reconfiguration technology, which is the significant feature of the newly designed Integrated Modular Avionics (IMA) system, enables the transfer of avionics functions from the failed module to the residual normal module, thereby enhancing the robustness of the whole system. The basic target of the IMA reconfiguration is to ensure the safe flight and correct execution of the mission. To solve the problem of lack of effective management mechanism for the IMA system development and safety assessment, a safety analysis method based on STAMP/STPA and UPPAAL for IMA reconfiguration is proposed. The method focuses mainly on system characteristics and multiparty interactions. On the basis of this approach, some studies and analyses have been carried out. Firstly, the STAMP/STPA principle is studied and used to identify unsafe control actions in the reconfiguration process. Secondly, a formal model of IMA reconfiguration is developed using UPPAAL. Finally, the accessibility analysis of the formal model is used to analyze UCAs and the corresponding loss scenarios. The method enables a detailed description of the interactions between the components and a rigorous mathematical analysis of the system, thereby diluting the effect of human factors while ensuring the accuracy and reliability of the safety constraints.

With the continual enhancement of the onboard avionics, the minimum flight crew has been downsized from five to two-person crew mode, and reduced crew operation has drawn extensive attention from aviation experts. Single-pilot operation (SPO) mode warrants careful account and research. This study investigated the intention modeling of commercial aviation single pilot based on the bidirectional long short-term memory (BiLSTM), mining the intention tendency of pilots’ behavior through artificial intelligence technology. This was done to avoid safety hazards caused by different intents and inconsistent operations of the single pilot and the cockpit automation system. The classification task of a single pilot’s behavior is the core of intention recognition. Various operation items contribute differently to the classification. To construct the interaction dataset and encode it into time series features, a single-pilot experiment is specifically performed, wherein the experience of an expert is summarized into single-pilot intent labels. The deep information in the feature vector of a single-pilot operation item is captured by the BiLSTM network, and the neural weight is adaptively assigned by the training mechanism. The operation sequence with the feature data is finally loaded into the softmax layer for intention classification. The proposed method is evaluated against long short-term memory (LSTM), term frequency-inverse document frequency (TF-IDF), convolutional neural network (CNN), Naive Bayesian (NB), and distributed representation’s intention modeling techniques. Because the proposed methods have higher F1 scores, the model can effectively share real-time information about the single-pilot intention with the cockpit automation system.

An analytical method is introduced to assess the susceptibility of radio altimeter (RA) receivers to adjacent-band fifth-generation (5G) signal interference and to quantify its impact on RA performance. The power-series method is employed to analyze the intermediate frequency (IF) signal gain compression effect of 5G signal interference on RA receivers. A behavioral-level simulation model of the RA receiver’s radio frequency (RF) front-end is constructed based on the advanced design system (ADS), and a 5G signal injection simulation is performed. The simulation results indicate that 5G signals can induce nonlinear effects in the RF front-end circuit of the RA, leading to IF signal gain compression, thereby affecting the subsequent signal processing of RA receivers. The interference effect on the RA receiver is influenced by factors such as the power and frequency of the 5G interference signal. To investigate this, an interference injection test was conducted on a specific RA receiver to validate the aforementioned interference mechanisms. The test results indicate that when the average power of the injected 5G signal at a frequency of 4000 MHz reaches −16 dBm, the IF signal power is significantly reduced. As the power of the 5G signal increases, this nonlinear effect becomes more pronounced. Furthermore, the height error ratio significantly increases, with consistent trends observed across different test frequencies. The interference threshold for the RA is lower when the signal frequency is closer to the RA operational signal frequency. Our research results demonstrate the efficacy of this method, providing a reference basis for studies on interference mechanisms and the evaluation of interference effects related to RA receivers within the electromagnetic environment of 5G signals.

In recent years, the integrated modular avionics (IMA) concept has been introduced to replace the traditional federated avionics. Different avionics functions are hosted in a shared IMA platform, and IMA adopts partition technologies to provide a logical isolation among different functions. The IMA architecture can provide more sophisticated and powerful avionics functionality; meanwhile, the failure propagation patterns in IMA are more complex. The feature of resource sharing introduces some unintended interconnections among different functions, which makes the failure propagation modes more complex. Therefore, this paper proposes an architecture analysis and design language- (AADL-) based method to establish the reliability model of IMA platform. The single software and hardware error behavior in IMA system is modeled. The corresponding AADL error model of failure propagation among components, between software and hardware, is given. Finally, the display function of IMA platform is taken as an example to illustrate the effectiveness of the proposed method.

For the air traffic management cyber physical system, if an attacker successfully obtains authority or data through a cyber attack, combined with physical attacks, it will cause serious consequences. Game theory can be applied to the strategic interaction between two parties, especially if the two parties have different goals. The offensive and defensive game process of the air traffic management cyber physical system is a non-cooperative and incomplete information dynamic game. The attacker can choose to camouflage the type of attack launched. The attack detection device configured in the system has a certain probability that the attack type can be successfully detected. According to the type of attack detected, the defender updates the posterior belief of the attack type and selects the corresponding protective strategies. According to the game process of offense and defense, a dynamic Bayesian game model of the air traffic management cyber physical system is established, the possible perfect Bayesian Nash equilibrium and its existence conditions are solved, and a complete mathematical model is constructed. The analysis shows that the dynamic Bayesian game model of the air traffic management cyber physical system can help the system defender to quickly obtain an equilibrium strategy and reduce the loss of the system as much as possible.

The integrated modular avionics (IMA) has been widely deployed on the new designed aircraft to replace the traditional federated avionics. Hosted in different partitions which are isolated by the virtual boundaries, different functions are able to share the common resources in the IMA system. The IMA system can dynamically reconfigure the common resources to perform the hosted functions when some modules fail, which makes the system more robust. Meanwhile, the reliability of the reconfigurable integrated modular avionics becomes more complicated. In this paper, we firstly model the IMA as a joint (m,k)-failure tolerant system with the consideration of its reconfigurable capability. Secondly, the continuous-time Markov chains are introduced to analyze the reliability of the IMA system. Thirdly, we take the comprehensive display function hosted in the IMA system as an example to show the practical use of the proposed reliability analysis model. Through the parameter sensitivity analysis, different failure rate λ and priority order of different modules are chosen to analyze their impact on system reliability, which can provide guidance to improve the reliability of the IMA system during a dynamic reconstruction process and optimize resource allocation.