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.

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.

Current infrared touch screen has problems in the multi-touch recognition and causes zigzag trace when drawing. In order to deal with these problems, an optimal algorithm based on target tracking was presented combined with Kalman filter and validation region algorithm. In data association, the validation region algorithm was used to match the right touch point's estimate value with the original target trajectory and delete the wrong touch point's estimate value. With the touch point motion model, Kalman filter was adopted for track smoothing and target movement prediction. Compared to the original recognition algorithm, the time of single touch point recognition was added about 3 μs by the optimal algorithm, but the smoothness in the trajectory angles was improved and about 60% of the burr amount was decreased. The experimental results show that some problems of the infrared touch screen are solved by the optimal algorithm, the effectiveness for multi-touch applications and trajectory smoothness is demonstrated, and the drawing effectiveness on the infrared touch screen is improved.