A type of multi-station TDOA passive location method and its Geometric Dilution of Precision (GDOP) has been studied. The key factors, formation of the aircrafts, the leader aircraft chosen, and the length and direction of the baseline which affect the position accuracy have been compared by computer simulation analysis. The results demonstrate that: to use T or the rhombus formation is optimum for improving the TDOA part of location accuracy when having a location task in a specified area, to use Y formation is preferred for ensuring location accuracy in all directions when surveying and searching throughout large airspace. In order to improve location accuracy it's essential to maintain the baseline direction and change the length of the baseline according to the real-time measured target angle.

With the decreasing size of manufacturing process, the scale of island-style field programmable gate array (FPGA) becomes larger, which leads to the increasing complexity of FPGA routing resources, especially hex programmable interconnect points (PIPs). Hex PIPs which span six tiles of the island-style FPGA have complex interconnect rules. Accordingly, research on complete hex PIPs test is rarely involved in the study of routing resources test. Therefore, this paper analyzes the hex PIPs architecture of the island-style FPGA, summarizes the interconnect rules of the hex PIPs mathematically in a two-dimensional coordinate system, and presents two proper test algorithms at the same time. The hex PIPs are divided into three directions, that is, horizontal, vertical, and oblique. According to the proposed coordinate equations, a cycle test structure in the horizontal and vertical directions and a test structure with partial-cascade patterns in the oblique direction are designed respectively. It is concluded that the proposed methods can achieve 100% fault coverage for the hex PIPs test in all directions, and the configuration number for hex lines test with the same methods is significantly decreased than previous researches.

Focusing on the problem incurred during particle swarm optimization (PSO) that tends to fall into local optimization when solving Nash equilibrium solutions of games, as well as the problem of slow convergence when solving higher order game pay off matrices, this paper proposes an improved Predator-Prey particle swarm optimization (IPP-PSO) algorithm based on a Predator-Prey particle swarm optimization (PP-PSO) algorithm. First, the convergence of the algorithm is advanced by improving the distribution of the initial predator and prey. By improving the inertia weight of both predator and prey, the problem of “precocity” of the algorithm is improved. By improving the formula used to represent particle velocity, the problems of local optimizations and slowed convergence rates are solved. By increasing pathfinder weight, the diversity of the population is increased, and the global search ability of the algorithm is improved. Then, by solving the Nash equilibrium solution of both a zero-sum game and a non-zero-sum game, the convergence speed and global optimal performance of the original PSO, the PP-PSO and the IPP-PSO are compared. Simulation results demonstrated that the improved Predator-Prey algorithm is convergent and effective. The convergence speed of the IPP-PSO is significantly higher than that of the other two algorithms. In the simulation, the PSO does not converge to the global optimal solution, and PP-PSO approximately converges to the global optimal solution after about 40 iterations, while IPP-PSO approximately converges to the global optimal solution after about 20 iterations. Furthermore, the IPP-PSO is superior to the other two algorithms in terms of global optimization and accuracy.