The traditional point-to-point saturation attack is not ideal choice facing high-density and multi-azimuth swarming intelligence targets. The maximum killing effect with weapon number less than target number can be achieved by selecting the appropriate types of weapons and the location of aiming points to realize the fire coverage. Considering the operational requirements of security targets, damage threshold and preference assignment, the Constrained Multi-objective Weapon-Target Assignment (CMWTA) mathematical model was established at first. Then, the calculation method of the constraint violation value was designed, and the individual coding, detection and repair as well as constraint domination were fused to deal with multiple constraints. Finally, the convergence metric for multi-objective weapon-target assignment model was designed, and the approaches were verified by the frameworks of Multi-Objective Evolutionary Algorithm (MOEA). In the comparison of three MOEA frameworks, the capacity of the Pareto sets of SPEA2 (Strength Pareto Evolutionary Algorithm Ⅱ) is mainly distributed in [21,25], that of NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm Ⅱ) is mainly distributed in [16,20], and that of MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition) is less than 16. In the verification of the repair algorithm, the algorithm makes the convergence metrics of three MOEA frameworks increased by 20 %, and the proportion of infeasible non-dominated solutions in Pareto solution set of 0%. The experimental results show that SPEA2 outperforms NSGA-Ⅱ and MOEA/D on distribution and convergence metric in solving CMWTA model, and the proposed repair algorithm improves the efficiency of solving feasible non-dominated solutions.

There exist some problems in the process of Particle Filter (PF), such as particle weight degeneracy, curse of dimensionality, and high computational cost. By constructing a logarithmic homotopy function, particle flow filter can avoid the problem of particle weight degeneracy, but it relies on the observation equation too much when solving the boundary value problem, and has poor effect when the noise is high. To address these problems, an improved particle flow filter algorithm was proposed. Firstly, an "innovation error" structure was introduced into the process of particle flow, so that the update of each particle is independent. Then, the Galerkin finite element method was utilized to obtain the numerical solution of the boundary value problem, so as to avoid the numerical instability problem that may be caused by the fitting sample prior. Finally, the performance of the improved algorithm was tested in the common nonlinear filter model and the maneuvering target tracking model. Simulation results show that the improved algorithm can suppress the dependency of the system on observation information, and has relatively good results with increasing noise, which effectively improve the filtering accuracy, and in multi-dimensional target tracking cases, the algorithm's computational efficiency and filtering accuracy are higher than those of the standard particle filter.

In order to solve the problems of having nonlinear observation equations and being susceptible to initial value of filtering in bearings-only target tracking, a range-parameterized hybrid coordinates Square Root Cubature Kalman Filter (SRCKF) algorithm was proposed. Firstly,it applied the SRCKF to hybrid coordinates,obtained better tracking effect than the SRCKF under Cartesian coordinates. And then it combined the range parameterization strategy with the SRCKF under hybrid coordinates, and eliminated the impact of unobservable range. The simulation results show that the proposed algorithm can significantly improve the accuracy and robustness although the computational complexity increases slightly.