On AUV Control with the Aid of Position Estimation Algorithms Based on Acoustic Seabed Sensing and DOA Measurements

Abstract

This article discusses various approaches to the control of autonomous underwater vehicles (AUVs) with the aid of different velocity-position estimation algorithms. Traditionally this field is considered as the area of the extended Kalman filter (EKF) application: It became a universal tool for nonlinear observation models and its use is ubiquitous. Meanwhile, the specific characteristics of underwater navigation, such as an incomplete sets of measurements, constraints on the range metering or even impossibility of range measurements, observations provided by rather specific acoustic beacons, sonar observations, and other features seriously narrow the applicability of common instruments due to a high level of uncertainty and nonlinearity. The AUV navigation system, not being able to rely on a single source of position estimation, has to take into account all available information. This leads to the necessity of various complex estimation and data fusion algorithms, which are the matter of the present article. Here we discuss some approaches to the AUV position estimation such as conditionally minimax nonlinear filtering (CMNF) and unbiased pseudo-measurement filters (UPMFs) in conjunction with velocity estimation based on the seabed profile acoustic sensing. The presented estimation algorithms serve as a basis for a locally optimal AUV motion control algorithm, which is also presented. environment, all electromagnetic (and therefore video tools) have a very restricted range of operation, and only acoustic waves permit sensing and communication for rather long distances. The problem is that acoustic measurements are notably barren in comparison with the electromagnetic ones: They do not have such features as color or contrast and just measure the direction of arrival (DOA) and/or the distance from the vehicle to the reflecting surface, such as obstacle, seabed, or ship. Therefore, even using multiple irradiating sensors like acoustic sonars, one can only obtain an image of distances to the seabed relief. An introduction of acoustical beacons with known positions can help to determine the own AUV position by using an angular observation filter, and this is the area of the (short and/or long) baseline methods application [2]. These methods are especially effective in the limited area of operation; moreover, they may be used in the case of a moving baseline established on a ship [3] and even in docking maneuvering [4]. Acoustic methods used in active mode give access to the distance measurements, which make the position estimation problem much easier, though in some areas of application such measurements are not acceptable or must be restricted due to the energetic or masking constraints [5].The possibility of the sonar employment in AUV navigation has already been mentioned by some authors either in application to the situation, when an accurate seabed map is available, or in the case of presence of significant seabed features [6]. Moreover, the active pinging can be used to determine one's speed over the seabed by ...

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

“…The idea of this method was first suggested in [64]. Further mathematical developments were given in [65]. We emphasize that this method uses a well-known optical flow approach from UAV practice.…”

“…Of course, video filming in underwater vehicles is not possible in a way similar to the surface applications since the opacity of water media only allows objects to be observed within distances of less than 5 m. However, high-resolution sonar, which creates a distance map from the device to the seabed, can be a source of such "video information". In our recent works [65], we proposed a distance-map-based method for the control and navigation of an AUV and provided comparison with traditional approaches. Thus far, as there are no explicit globally optimal solutions for the stochastic problems with nonlinear observations, we have considered the control and position estimation problems in the locally optimal formulation.…”

“…Here, we propose algorithms for position estimation formulated on the UAV linear dynamic model [65] and the external bearing-only measurements given by a passive acoustic DOA estimation device [61]. Consider that we know the coordinates of the predeployed stationary acoustic beacon X B = (X B , Y B , Z B ) T .…”

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

“…On-board aiding sensors, Doppler velocity log, pressure sensor, and magnetometers can also help to reduce the effect of IMU drift, but all these sensors are affected by noise. To improve the navigation accuracy and to minimize disturbance because of noise, EKF-based algorithms are the most commonly used in underwater navigation and localization [ 14 , 15 , 16 , 17 , 18 , 19 ].…”

Passive Underwater Target Tracking: Conditionally Minimax Nonlinear Filtering with Bearing-Doppler Observations

“…This algorithm was initially proposed for the state filtering of the discrete-time stochastic finite-dimensional [35] and distributed [36] observation systems. Recently, it was successfully applied to the tracking of a maneuvering aerial target given the radar observations [37] and the navigation of the Automated Underwater Vehicle (AUV) [38].…”