基于樽海鞘群算法的无源时差定位

陈涛; 王梦馨; 黄湘松

doi:10.11999/JEIT170979

基于樽海鞘群算法的无源时差定位

doi: 10.11999/JEIT170979

(哈尔滨工程大学信息与通信工程学院哈尔滨 150001)

基金项目:

国家自然科学基金项目(61571146)，中央高校基本科研业务费专项基金(HEUCFP201769)

详细信息

作者简介:
陈涛：陈涛：男，1974年生，教授，博士生导师，研究方向为宽带信号检测、处理与识别. 王梦馨：女，1994年生，硕士生，研究方向为宽带信号检测、无源定位. 黄湘松：女，1980年生，讲师，研究方向为无源定位，语音、图像信号处理.

中图分类号: TN971
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出版历程
- 收稿日期: 2017-10-20
- 修回日期: 2018-03-30
- 刊出日期: 2018-07-19

Time Difference of Arrival Passive Location Based on Salp Swarm Algorithm

CHEN Tao WANG Mengxin HUANG Xiangsong

Funds:

The National Natural Science Foundation of China (61571146), The Fundamental Research Funds for the Central Universities (HEUCFP201769)

摘要

摘要: 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。
- 无源定位 /
- 到达时差 /
- 智能优化算法 /
- 樽海鞘群算法
Abstract: 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.
- Passive location、Time-Difference-Of-Arrival (TDOA)、Intelligence optimization algorithm、Salp- Swarm-Algorithm (SSA) /

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参考文献(16)

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