An Ultra-Short Baseline Underwater Positioning System with Kalman Filtering

Abstract

The ultra-short baseline underwater positioning is one of the most widely applied methods in underwater positioning and navigation due to its simplicity, efficiency, low cost, and accuracy. However, there exists environmental noise, which has negative impacts on the positioning accuracy during the ultra-short baseline (USBL) positioning process, which results in a large positioning error. The positioning result may lead to wrong decision-making in the latter processing. So, it is necessary to consider the error sources, and take effective measurements to minimize the negative impact of the noise. In our work, we propose a USBL positioning system with Kalman filtering to improve the positioning accuracy. In this system, we first explore a new kind of element array to accurately capture the acoustic signals from the object. We then organically combine the Kalman filters with the array elements to filter the acoustic signals, using the minimum mean-square error rule to obtain accurate acoustic signals. We got the high-precision phase difference information based on the non-equidistant quaternary original array and the phase difference acquisition mechanism. Finally, on account of the obtained accurate phase difference information and position calculation, we determined the coordinates of the underwater target. Comprehensive evaluation results demonstrate that our proposed USBL positioning method based on the Kalman filter algorithm can effectively enhance the positioning accuracy.

Navigation of Underwater Drones and Integration of Acoustic Sensing with Onboard Inertial Navigation System

“…For a standalone SINS, its positioning error accumulates over time; nevertheless, the high-precision positioning technology needs the application of statistical methods for isolation of nonproperly operating sensors [22]. In the work [23], it is shown that the high-precision phase difference information can be obtained based on the non-equidistant quaternary array and the phase difference acquisition mechanism.…”

Positioning Combination Method of USBL Using Four-Element Stereo Array

“…The second new array is shown in Figure 3. According to formula (6), under the influence of noise when 12 14 13 2 cos…”

“…Zheng Enming [10] optimized the traditional array elements, basically using small spacing array elements to solve ambiguity and large spacing array elements to improve positioning accuracy. Luo Qinghua [11,12] introduced Kalman filter-based methodology on the array designed by Zheng Enming to improve the positioning accuracy, but these improvements do not improve the poor localization performance of signals at a large incident angle. Liang Guolong [13] proposed using depth information to assist localization to improve the positioning accuracy by up to 0.25%.…”

A SINS/DVL/USBL Integrated Navigation and Positioning IoT System with Multiple Sources Fusion and Federated Kalman Filter

“…And this can not satisfy the increasing needs from applications [5][6]. Since there are many different kinds of navigation and positioning subsystems that have specific characteristics respectively [7], such as SINS, DVL, visual navigation, and USBL [8], we can integrate these subsystems to enhance the overall accuracy and the robustness of the positioning method. Through an integrated approach, we can get higher accuracy and stability of the navigation and positioning system in a long time by performing information fusion and filter correction.…”