Radar Warning Receiver (RWR) is one of the basic pieces of combat equipment necessary for the electromagnetic situational awareness of aircraft in modern operations and requires good rapid performance and accuracy. This paper proposes a data processing flow for radar warning devices based on a hierarchical processing mechanism to address the issue of existing algorithms’ inability to balance real-time and accuracy. In the front-level information processing module, multi-attribute decision-making under intuitionistic fuzzy information (IFS) is used to process radar signals with certain prior knowledge to achieve rapid performance. In the post-level information processing module, an improved tri-training method is used to ensure accurate recognition of signals with low pre-level recognition accuracy. To improve the performance of tri-training in identifying radar emitters, the original algorithm is combined with the modified Hyperbolic Tangent Weight (MHTW) to address the problem of data imbalance in the radar identification problem. Simultaneously, cross entropy is employed to enhance the sample selection mechanism, allowing the algorithm to converge rapidly.

There are massive data and rapidly changing battlefield situations in modern electronic warfare, which is a challenge to jamming resource allocation. It is difficult for the existing optimization algorithms to balance optimization capability and calculation speed at the same time. To solve this problem, this study proposes an improved genetic selection electronic warfare operator hyperheuristic (GAEWHH) algorithm. As an emergent optimization algorithm, the hyperheuristic (HH) framework has not previously been applied to the problem of jamming resource allocation. This is a two-level algorithm framework that can isolate problem domains. The high level uses an improved genetic algorithm to search the heuristic space, and four electronic warfare operators (EWOs) based on the problem domain are designed for the low level to search the solution space. Combining different EWOs can change the population diversity, evolution direction and algorithm complexity of the GAEWHH algorithm, which improves the algorithm performance to meet battlefield situation requirements. The experiment shows that for large-scale problems, the GAEWHH algorithm is better than the mainstream evolutionary algorithm in terms of optimization capability and better than Google OR-Tools in terms of calculation speed. In this way, the GAEWHH algorithm achieves a balance between optimization capability and calculation speed.

The degradation properties of Indium phosphide hetero-junction bipolar transistors (InP HBTs) under proton irradiation are studied and modelled using a compact model for pre-irradiation, post-irradiation, and post-annealing. The variation rates of the model parameters, such as the base–emitter saturation current (I_SE) and ideality factor in the ideal region (N_E) in the forward Gummel characteristics, the zero-biased capacitance (C_je) and the grading factor (M_jer) in the BE junction capacitance, and the transit time parameter in the base region (T_fb), are analysed to delve into the degradation mechanism induced by proton irradiation. The displacement damage, induced by proton irradiation in the space charge region of the base–emitter junction and in the quasi-neutral bulk base region, is found to be responsible for the decrease in current gain and cut-off frequency. After annealing, the variation rates of the parameters decrease significantly compared to post-irradiation. This suggests that the recombination of unstable defects leads to a slight recovery in the degradation characteristics of InP HBTs after a period of annealing.

Modern cognitive electronic reconnaissance methods for radar systems must contend with the complex electromagnetic environments arising from the deployment of multiple signal sources and radar countermeasures, which greatly limit access to the degree of prior information required to enable effective target recognition. The present work addresses this issue by proposing a multiperspective collaborative clustering method for sorting radiation sources based on the multiperspective information of radar signals. In contrast to conventional collaborative training approaches, which are suitable only for semisupervised learning, the proposed multiperspective collaborative clustering method performs unsupervised clustering, cluster label transfer, and dimensionality reduction by linear discriminant analysis iteratively based on the differences between the clustering results obtained from two signal perspectives radiation signal sorting can be conducted in a noncooperative context. The results of comparative experiments demonstrate that the proposed multiperspective sorting method can make full use of the difference information between basic signal characteristics and intrapulse features and thereby improve the accuracy of clustering-based radiation source sorting. Accordingly, the sorting ability of the proposed method is superior to those of other state-of-the-art clustering methods and that of the single-perspective clustering-based sorting method.