Particle Swarm and Monte Carlo Optimized Mobile Localization Algorithm in Underwater Acoustic Sensor Networks

HAO Shi-ya; YANG Yuan-yuan; DONG Yi-jing; ZHAO Xiao-xu; CHEN Jia-xing

doi:10.12263/DZXB.20200030

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Particle Swarm and Monte Carlo Optimized Mobile Localization Algorithm in Underwater Acoustic Sensor Networks

更新时间：2025-12-08

- Particle Swarm and Monte Carlo Optimized Mobile Localization Algorithm in Underwater Acoustic Sensor Networks
- Acta Electronica Sinica Vol. 49, Issue 2, Pages: 292-299(2021)
- 作者机构：
  
  1. 河北师范大学计算机与网络空间安全学院,河北,石家庄,050024
  2. 河北师范大学数学科学学院,河北,石家庄,050024
  3. 河北师范大学中燃工学院,河北,石家庄,050024
  4. 石家庄科技工程职业学院,河北,石家庄,050800
  5. 河北师范大学计算机与网络空间安全学院,河北,石家庄,050024
  6. 河北师范大学数学科学学院,河北,石家庄,050024
  7. 河北师范大学中燃工学院,河北,石家庄,050024
  8. 石家庄科技工程职业学院,河北,石家庄,050800
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61771181, No.61701165, No.62071167)
- DOI：10.12263/DZXB.20200030
  CLC： TP393
- Published Online：25 February 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
HAO Shi-ya, YANG Yuan-yuan, DONG Yi-jing, et al. Particle Swarm and Monte Carlo Optimized Mobile Localization Algorithm in Underwater Acoustic Sensor Networks[J]. Acta Electronica Sinica, 2021, 49(2): 292-299.
DOI：

HAO Shi-ya, YANG Yuan-yuan, DONG Yi-jing, et al. Particle Swarm and Monte Carlo Optimized Mobile Localization Algorithm in Underwater Acoustic Sensor Networks[J]. Acta Electronica Sinica, 2021, 49(2): 292-299. DOI： 10.12263/DZXB.20200030.

摘要

针对水声传感器网络中移动定位算法的误差和鲁棒性问题，提出两种蒙特卡罗移动定位算法：CRMCL（Circular Ring Monte Carlo Localization）和PRMCL（Particle Swarm Optimization for Circular Ring Monte Carlo Localization）.CRMCL利用1跳锚节点构建圆形采样区域和圆环过滤器.通过定义样本密度得到合理的样本数，论证圆环参数与过滤区域面积的关系.通过仿真实验得到合理的圆环参数，并以此构建高效的过滤器，降低定位误差.PRMCL使用粒子群算法优化CRMCL过滤后的样本，降低了无效样本的数目，增强了算法的鲁棒性.仿真表明，在不需要额外硬件的情况下，CRMCL和PRMCL比蒙特卡罗及其改进算法误差小、鲁棒性强.

Abstract

Aiming at error and robustness of localization algorithms in underwater acoustic sensor networks

we proposed two monte carlo mobile localization algorithms: Circular ring monte carlo localization (CRMCL) and particle swarm optimization for circular ring monte carlo localization (PRMCL). CRMCL used one-hop anchor nodes to construct circular sampling area and ring filter. By defining sampling points density

we obtained reasonable sample number. The relationship between the ring parameter and the filtration area was demonstrated. Then the reasonable ring parameter was obtained through the simulation experiments

and an efficient filter was constructed to reduce the localization error. PRMCL used particle swarm optimization (PSO) algorithm to optimize the samples filtered by CRMCL

which reduced the number of invalid samples and improved the robustness of localization. The simulation results show that CRMCL and PRMCL have lower localization error and better robustness than monte carlo localization and other improved algorithms without additional hardware.

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