基于樽海鞘群算法的无源时差定位

针对无源时差(TDOA)定位的非线性方程解算问题，论文使用一种名为樽海鞘群算法(SSA)的新的群体智能优化算法。首先，该算法采用一种新的群体更新模型，充分平衡迭代过程中的探索行为与开发行为，在保证搜索的全局性与个体的多样性的同时，改善了其他智能优化算法容易陷入局部极值的问题。其次，该算法控制参数很少，运算速度明显提高。该算法的收敛速度十分稳定，定位精度更高。仿真结果表明，樽海鞘群算法在3维时差定位中能够快速、稳定地收敛至目标位置，对传统粒子群算法(PSO)、改进的线性权重粒子群算法(IPSO)与SSA的定位精度进行比较，SSA精度明显高于PSO与IPSO。

Time Difference of Arrival Passive Location Based on Salp Swarm Algorithm

To solve the nonlinear equation problems of Time-Difference-Of-Arrival (TDOA) passive location, a new swarm intelligence optimization algorithm called Salp-Swarm-Algorithm (SSA) is used. Firstly, a new renewal model of salps is proposed to balance exploration and exploitation properly during iteration in SSA. SSA not only ensures the wholeness of searching and the diversity of individuals, but also improves the problem that other intelligent optimization algorithms fall into local optima easily. Besides, there are few parameters to be adjusted, therefor, the computation speed is obviously improved. Moreover, the convergence performance of the proposed algorithm is very stable and the accuracy of location is higher. Simulation results show that the proposed algorithm can converge to the position of emitters fast and stably in 3D TDOA location. Comparing with Particle-Swarm- Optimization (PSO) and Improved-Particle-Swarm-Optimization (IPSO), the proposed algorithm has lower mean square error.