Underwater TDOA/AOA joint localization algorithm based on hybrid invasive weed optimization algorithm

• Published in: IET communications Vol. 15; no. 19; pp. 2376 - 2389
• Main Authors: Zhang, Zhenkai, Lin, Yunhang, Jin, Biao
• Format: Journal Article
• Language: English
• Published: Stevenage John Wiley & Sons, Inc 01.12.2021; Wiley
• Subjects: Algorithms; Localization; Localization method; Lower bounds; Maximum likelihood method; Optimisation techniques; Optimization algorithms; Other topics in statistics; Radiation; Sensors; Signal detection; Signal processing; Signal processing and detection; Underwater; Wireless sensor networks
• ISSN: 1751-8628; 1751-8636; 1751-8636
• DOI: 10.1049/cmu2.12277

In order to achieve precise localization in the underwater environment, an underwater localization method based on hybrid invasive weed algorithm is proposed. First, considering the insufficient local search capability of the invasive weed algorithm, the salp swarm algorithm is used to pre‐process the initial population, the position and velocity update formulas are employed to further improve the search depth, and the hybrid invasive weed algorithm is obtained. Then, the localization error is designed as the objective function to obtain the initial value of the source, and a new localization model is built using the initial values and interference parameters. Finally, an improved two‐step weighted least square method is used to obtain the precise location of the source. The performance of the algorithm is also verified by comparisons with Cramer‐Rao Lower Bound. Simulation experiments show that the proposed algorithm has better localization accuracy than other classic methods.