This paper investigates the threat assessment method and target assignment algorithm in multi-UAV cooperative air combat decision-making. To address the uncertainty and dynamic changes in multiple threat attributes and attribute information of UAV targets, we propose a UAV target dynamic threat assessment method based on intuitionistic fuzzy multiattribute decision-making. Firstly, we propose a mixed situation information representation method to represent interval-valued fuzzy data appropriately. Secondly, we employ the normal distribution weight assignment method to fuse the multi-time situation information. Then, by incorporating the analytic hierarchy process and entropy method, we determine the normalized threat value of the target considering both objective situation data characteristics and decision-maker preferences. Finally, a simulation example is provided to validate the rationality of our proposed algorithm. For solving the multi-weapon multi-target assignment problem, a target assignment method based on the VNS-IBPSO algorithm is introduced. This method improves upon the limitations of the BPSO algorithm, such as limited local search capability and premature convergence, by combining variable neighborhood search and an improved binary particle swarm optimization algorithm. Simulation results show that the proposed threat assessment method can obtain reasonable threat assessment results under complex dynamic environments. The proposed VNS-IBPSO algorithm can solve the target assignment model quickly and efficiently based on the assessment results, therefore ensuring that the UAV mission planning system makes the correct combat plan.

For maneuvering target tracking, the interactive multiple model (IMM) algorithm and its variants have shown good performance, among which the variable structure IMM (VSIMM) algorithm is the most widely studied one. The approximation degree of the algorithm and the matching degree of the model subset will affect the performance of the VSIMM. In addition, considering that smoothing can provide better estimation of the target state, a new adaptive VSIMM filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. First, an accurate model of the VSIMM algorithm is established, in which the IMM is run in parallel with and independently from the different model subsets, and the state estimation of the model subset with the highest probability is selected as the final estimation result. Then, an adaptive VSIMM (AVSIMM) algorithm was designed based on the VSIMM algorithm. The adaptation is reflected in the construction of a new model subset from the original model subsets, which improves the matching degree between the model subset and the actual maneuvering model of the target. Finally, by smoothing the filtering data of the AVSIMM algorithm, the AVSIMMFS algorithm is obtained. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Simulation results show that the tracking performance of the AVSIMMFS algorithm is better than that of other methods.

In the descent phase of the hypersonic target flight trajectory, the hypersonic speed of the target makes the reaction time shorter. Due to overload restrictions, it is challenging for the interceptor to achieve successful interception. To address this problem, a predictive differential game guidance approach based on dynamic optimization algorithm is proposed to relax the interceptor acceleration requirements. Two-dimensional kinematics and dynamics engagement mode for hypersonic target interception is formulated as a nonlinear differential control model in the presence of matched uncertainties. The nonlinear differential model is transformed into a differential game model by combining a performance index. The performance index is positively correlated with the Line-Of-Sight (LOS) rate and control effort of interceptor and negatively correlated with the target maneuver. The Chaotic Quantum Particle Swarm Optimization (CQPSO) algorithm is utilized to address the nonlinear differential game problem to generate the instantaneous and predicted guidance commands for the interceptor and target. Numerical examples are given to verify the performance of the proposed guidance law in various engagement scenarios, including different target maneuvers, initial heading angles and target overload. Moreover, the performance of the algorithm is validated comparing with traditional and state-of-the-art guidance laws.

Considering that the terminal impact angle constraint can improve the interception performance of hypersonic target, a novel particle swarm optimization guidance (NPSOG) algorithm is proposed to satisfy the impact angle constraint. Two-dimensional dynamics engagement mode for hypersonic target interception is formulated. The performance index is positively correlated with the line-of-sight (LOS), LOS rate, and the relative distance between missile and target. The weight coefficients among the three are adaptively adjusted by the fuzzy logic controller. The particle swarm optimization (PSO) algorithm is utilized to generate the guidance commands. Numerical examples are given to verify the performance of the proposed guidance law in various engagement scenarios, and the performance of the algorithm is validated comparing with several heuristic guidance methods and nonheuristic guidance methods.

For a defender protecting a target from an incoming high-maneuvering homing missile, an Optimal Predictive Guidance Law (OPGL) is proposed based on a guidance law identification algorithm. First, a guidance law identification algorithm based on the Interactive Multiple Guidance law Model (IMGM) is designed. IMGM uses multiple sub-filters for parallel processing, and there is interaction between the inputs and outputs of the sub-filters. Second, the VSIMGM algorithm is further designed based on the IMGM algorithm. In this algorithm, a new guidance law model subset is constructed based on the original guidance law model set. By reducing the number of models, the model error is reduced, and the identification probability of the guidance law model is improved. Then, based on the identification result of the identification algorithm, the OPGL is given by using the variational method through the steps of linearizing the kinematic model and reducing the order of state equation. The OPGL reduces the overload requirement of the defender and improves interception accuracy without assuming the maneuver mode of a homing missile. Finally, the effectiveness of the proposed OPGL and guidance law identification algorithm based on VSIMGM is demonstrated using numerical simulation. Compared with the guidance law identification algorithm based on MMAE and IMGM, the guidance law identification algorithm based on VSIMGM can quickly, stably, and accurately identify the guidance law. In addition, based on the guidance law identification results of the homing missile, compared with proportional navigation, augmented proportional navigation, differential game guidance (DGG), and sliding mode guidance (SMG), OPGL can reduce the required overload of the missile and achieve accurate interception.