To address the problem that the traditional generalized cross correlation (GCC) method in ultra-short baseline (USBL) positioning systems has a poor delay estimation accuracy in a low signal-to-noise ratio environment or complex noise background, a generalized quadratic cross correlation (GQCC) time delay estimation algorithm based on signal preprocessing and fourth-order cumulants is proposed. The noisy signal was first preprocessed using singular value decomposition and wavelet denoising. Then, the delay was calculated using an algorithm that combined the GQCC and the fourth-order cumulant. The results of the simulation and sea trial demonstrate that the proposed method is superior to the GCC method and the GQCC method and may significantly increase the positioning accuracy of the USBL system. This technique can offer a fresh technological perspective for weak signal detection and passive positioning of small targets in ocean detection.
The installation error of an acoustic transceiver array is one of the important error sources in an ultra-short baseline (USBL) system. In a USBL system with a positioning accuracy of 0.5%, an installation error angle of 1° will lead to a positioning error of 1.7% times the slant distance. In this paper, a dual transponder-based installation angle error calibration method for USBL is proposed. First, the positioning errors induced by various installation angles are deduced and analysed using the linear measurement of seafloor targets. Then, an iterative algorithm is proposed that estimates the rolling alignment error, pitching alignment error, and heading alignment error, in that order. The simulation and experienced results show that, after three iterations, the estimates of the three alignment errors can converge quickly, all of the estimates converge to within 0.001° and the estimated values are very close to the true values. The horizontal positioning error caused by the installation error angle can be reduced by nearly 75%. The method has good effectiveness and robustness, and can greatly improve the positioning accuracy of the USBL system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.