Threat assessment is an important process of quantifying the threat of enemy attacking targets. It is also one of the main basis for commanders to make control decisions in air defense operations. Target threat assessment needs to obtain a large amount of air attack target information from various reconnaissance equipment and battlefield sensors, fuse these information, and get the ranking of the threat degree of air attack targets to our side. In view of the unbalanced distribution of index weight in threat assessment in air defense operations, a target threat assessment model based on combined weight is proposed in this paper. Firstly, according to the index system of air raid target threat assessment, the subjective and objective weights of the indexes are determined by analytic hierarchy process and critical method respectively, and the combined weights are calculated by multiplication synthesis method; Then the threat ranking of targets is obtained by TOPSIS method; Finally, the model is verified by an example. The simulation results show that the air target threat assessment model is reasonable.

Air strikes are among the main means of attack in modern warfare. To improve air defense capabilities and aid military decision-making, threat assessment models have been introduced. As the parameters of the kernel extreme learning machine (KELM) model need to be set individually, this study proposes a parameter learning strategy based on a multistrategy improved sparrow search algorithm (MISSA). First, a reasonable threat assessment model was established based on the capability and situation factors of air targets. Second, the sparrow search algorithm was improved in terms of population position initialization and position update strategy, incorporating tent chaos reverse learning, nonlinear inertia weights, a global search strategy, and adaptive t-distribution. The effectiveness of the MISSA strategy was verified using nine common benchmark functions. The results show that the proposed MISSA finds an effective balance between global and local searches. Moreover, when the MISSA is applied to solve the tuning problem of KELM, the values of mean absolute percentage error, mean square error, root mean square error, and mean absolute error for MISSA–KELM in the air target threat assessment problem are 2.013 × 10−2, 1.282 × 10−4, 1.132 × 10−2, and 8.316 × 10−3, respectively, all of which are higher than that of the other metaheuristic algorithms (e.g., ACWOA-KELM and HGWO-KELM). Therefore, the method proposed in this study can be used as a parameter-tuning tool for KELM, enabling KELM to perform better in practical applications.