Optimum frequency selection for localization of underwater AUV using dynamic positioning parameters

Rajasekaran, Logeshwaran; Santhanam, Sakthivel Murugan

doi:10.1007/s00542-021-05222-3

Cited by 3 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, for every positive integer N node , there exists an N node -node connected graph for which T N node (α) = Ω N 3 node log(1/α) . According to the definition of O and Ω in [32], the CF/DPF's computational complexity is obtained:…”

Section: Algorithm Analysismentioning

confidence: 99%

A Tracking Algorithm for Sparse and Dynamic Underwater Sensor Networks

Liu¹,

Xu²,

Liu³

2022

JMSE

View full text Add to dashboard Cite

An underwater sensor network (UWSN) has sparse and dynamic characteristics. In sparse and dynamic UWSNs, the traditional particle filter based on multi-rate consensus/fusion (CF/DPF) has the problems of a slow convergence rate and low filtering accuracy. To solve these problems, a tracking algorithm for sparse and dynamic UWSNs based on particle filter (TASD) is proposed. Firstly, the estimation results of a local particle filter are processed by a weighted average consensus filter (WACF). In this way, the reliability difference of state estimation between nodes in sparse and dynamic UWSN is reasonably eliminated. Secondly, a delayed update mechanism (DUM) is added to WACF, which effectively solves the problem of time synchronization between the two particle filters. Thirdly, under the condition of limited communication energy consumption, an alternating random scheme (ARS) is designed, which optimizes the mean square convergence rate of the fusion particle filter. Simulation results show that the proposed algorithm can be applied to maneuvering target tracking in sparse and dynamic UWSN effectively. Compared with the traditional method, it has higher tracking accuracy and faster convergence speed. The average estimation error of TASD is 91.3% lower than that of CF/DPF, and the weighted consensus tracking error of TASD is reduced by 85.6% compared with CF/DPF.

show abstract

Section: Algorithm Analysismentioning

confidence: 99%