Ultra-fast calculation method of incident angle based on underwater acoustic round-trip positioning

Liu, Yangfan; Subirana, Jaume Sanz; Xu, Tianhe; Wang, Junting; Yang, Wenlong; Zhang, Shengqiu; Shu, Jianxu

doi:10.1016/j.oceaneng.2024.117998

Cited by 1 publication

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As depicted in Figure 1, once the target depth is established, measuring the TDOA between the source on the AUV and various base array elements enables the derivation of quadratic equations that define a hyperbolic curve [10]. Assuming that the coordinates of the AUV are represented by (x, y, z) and those of the receiver by (x i , y i , z i ).…”

Section: Time Difference Of Arrival Systemmentioning

confidence: 99%

“…A Taylor expansion at the initially guessed target location (x 0 , y 0 ), which disregards components higher than the second order, is performed as follows. (10) where…”

Section: Chan-taylor Algorithmmentioning

confidence: 99%

“…These systems calculate the AUV's position by analyzing acoustic propagation times using algorithms and refining the estimates. Key technologies include time of arrival (TOA) [9], time difference of arrival (TDOA) [10], angle of arrival (AOA) [11], and received signal strength indicator (RSSI) [12]. TDOA is particularly valued for its simplicity, real-time capabilities, and independence from time synchronization between the sound source and sensors, making it useful not only in underwater positioning but also in terrestrial wireless systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of Hydroacoustic Localization Algorithms for AUV Based on the Error-Corrected WMChan-Taylor Algorithm

Yang,

Gao,

et al. 2024

JMSE

View full text Add to dashboard Cite

Autonomous underwater vehicles (AUVs) are susceptible to non-line-of-sight (NLOS) errors and noise bias at receiving stations during the application of hydroacoustic localization systems, leading to a degradation in positioning accuracy. To address this problem, this paper optimizes the Chan-Taylor algorithm. Initially, we propose the Weighted Modified Chan-Taylor (WMChan-Talor) algorithm, which introduces dynamic weights into the Chan algorithm to correct noise variance at measurement stations, thereby improving the accuracy of AUV positioning. Computer simulations validate the effectiveness of the WMChan-Taylor algorithm in enhancing positioning accuracy. To further address the accuracy degradation caused by noise deviations across different receiving stations, we introduce an error-corrected WMChan-Taylor algorithm. This algorithm utilizes a standard residual function to eliminate significant delays caused by large errors at receiving stations and applies standard residual weighting to improve the combined positioning solution. The performance of the error-corrected WMChan-Taylor algorithm is demonstrated through both computer and semi-physical simulation experiments, confirming its capability to isolate noisier stations and thus enhance overall positioning accuracy.

show abstract

Section: Time Difference Of Arrival Systemmentioning

confidence: 99%

“…A Taylor expansion at the initially guessed target location (x 0 , y 0 ), which disregards components higher than the second order, is performed as follows. (10) where…”

Section: Chan-taylor Algorithmmentioning

confidence: 99%