【摘要】 为了满足有限测点下声阵列定位精度提升的需求，提出了基于双种群量子粒子群(dual-group interaction quantum particle swarm optimization, DIR-QPSO)联合到达时差定位技术(time difference of arrival, TDOA)的单基站声阵列拓扑结构优化布设方法。首先，将声阵列中的声传感器作为粒子，利用Logistic混沌模型全局遍历性的优势初始化种群；其次，利用双种群之间信息共享优势，消除迭代过程中陷入局部最优点；再次，以TDOA模型构建适应度评价函数，得到声传感器最优布设位置；最后，通过仿真验证，得到优化后的声阵列拓扑结构。仿真结果表明，与传统六元正四棱锥阵列及QPSO优化后的阵列相比，方法将几何精度因子减小至1.351 8 m,克拉美罗下界减小至0.481 7 m,均方根误差减小至0.556 4 m。最后进行实验对比验证，实验结果表明，提出的单基站阵列具有更高的定位精度，极大提升了弹丸落点定位精度。更多还原

【Abstract】 In order to meet the demand for improving the positioning accuracy of acoustic arrays under limited measurement points, this paper proposes a single base station acoustic array topology optimization deployment method based on the dual-group interaction population quantum particle swarm optimization combined time of arrival localization technology. Firstly, the acoustic sensors in the acoustic array are treated as particles, and the population is initialized using the advantage of the global ergodicity of the Logistic chaotic model. Secondly, utilizing the advantage of information sharing between two populations to eliminate trapped local optima during the iterative process. Once again, construct a fitness evaluation function using the TDOA model to obtain the optimal placement position of the acoustic sensor. Finally, through simulation verification, the optimized acoustic array topology structure was obtained. The simulation results show that compared with the traditional hexagonal pyramid array and QPSO optimized array, this method reduces the geometric accuracy factor to 1.351 8 m, the CRLB is reduced to 0.481 7 m, and the root mean square error to 0.556 4 m.Finally, experimental comparison and verification were conducted, and the experimental results showed that the single base station array proposed in this paper has higher positioning accuracy, greatly improving the accuracy of projectile landing point positioning.更多还原