Sensor Misalignment-tolerant AUV Navigation with Passive DoA and Doppler Measurements

TL;DR

The paper tackles the challenge of robust AUV navigation when there is misalignment between the acoustic array and the attitude sensor. It presents a two-stage framework that first initializes beacon localization and alignment using nonlinear least squares, then performs real-time state estimation with an Unscented Kalman Filter that fuses passive DoA, Doppler, and depth measurements with dead reckoning. The main contributions are online beacon localization and online sensor alignment tolerant to misalignment, improved resilience to DVL outages via Doppler observations, and a rigorous observability analysis to guide trajectory design. Simulation and preliminary field tests demonstrate that the method maintains navigation accuracy under significant sensor misalignment and shallow elevation angles, offering a practical, low-cost approach for robust underwater navigation.

Abstract

We present a sensor misalignment-tolerant AUV navigation method that leverages measurements from an acoustic array and dead reckoned information. Recent studies have demonstrated the potential use of passive acoustic Direction of Arrival (DoA) measurements for AUV navigation without requiring ranging measurements. However, the sensor misalignment between the acoustic array and the attitude sensor was not accounted for. Such misalignment may deteriorate the navigation accuracy. This paper proposes a novel approach that allows simultaneous AUV navigation, beacon localization, and sensor alignment. An Unscented Kalman Filter (UKF) that enables the necessary calculations to be completed at an affordable computational load is developed. A Nonlinear Least Squares (NLS)-based technique is employed to find an initial solution for beacon localization and sensor alignment as early as possible using a short-term window of measurements. Experimental results demonstrate the performance of the proposed method.

Figures (11)

• Figure 1: An illustration of the acoustic measurement model. The acoustic array measures the bearing $\theta$ and the elevation $\phi$, as well as the Doppler speed $s$. There is a misalignment between the acoustic frame $\prescript{a}{}{(\cdot)}$ and the vehicle frame $\prescript{v}{}{(\cdot)}$
• Figure 2: Block diagram of the state initialization and the basic solution. Both of the two steps can be performed by AUV in real time
• Figure 3: Distribution of AUV positions during state estimation. We use the spherical coordinate system to code AUV positions with respect to the beacon. Each position is specified by the radial distance $r$ and two angles (i.e., the bearing angle $\prescript{b}{}{}\theta_v$ and the elevation angle $\prescript{b}{}{}\phi_v$ ). Note that the two angles describe the vehicle's direction relative to the beacon.
• Figure 4: Simulation results of the effects of range variation and angle variation. We utilize the ratio of the smallest singular value of the Jacobian matrix $\mathbf{Y}$ to the largest singular value to quantitatively evaluate the observability since a smaller ratio indicates that $\mathbf{Y}$ is closer to the situation $Rank(\mathbf{Y})<6$. Here different colors represent different values of this ratio. We denote the standard deviation of angles as $\sigma_a$, and the distribution of two angles follows $\left[\prescript{b}{}{}\theta_v\prescript{b}{}{}\phi_v \right]\sim \mathcal{N}(\left[00 \right] ,\left[\sigma_a^200\sigma_a^2 \right])$. The distribution of the radial distance obeys $r\sim \mathcal{N}(20, \sigma_a^2)$, where $\sigma_r$ is the standard deviation of the radial distance.
• Figure 5: Trajectory and attitude estimates of the proposed approach versus the ground truth in the simulation. Note that the errors of attitude angles versus the range are minimal, so we zoom in on some parts of the plotted curves.