Navigation State Research Articles

Probabilistic Map Matching for Robust Inertial Navigation Aiding

<h3>Abstract</h3> Robust aiding of inertial navigation systems in GNSS-denied environments is critical for the removal of accumulated navigation error caused by the drift and bias inherent in inertial sensors. One way to perform such an aiding uses matching of geophysical measurements, such as gravimetry, gravity gradiometry or magnetometry, with a known geo-referenced map. Although simple in concept, this map-matching procedure is challenging: The measurements themselves are noisy, their associated spatial location is uncertain, and the measurements may match multiple points within the map (i.e., non-unique solution). In this paper, we propose a probabilistic multiple-hypotheses tracker to solve the map-matching problem and allow robust inertial navigation aiding. Our approach addresses the problem both locally, via probabilistic data association, and temporally by incorporating the underlying platform kinematic constraints into the tracker. The estimated platform position from the output of map matching is then integrated into the navigation state using an unscented Kalman filter. Additionally, we present a statistical measure of local map information density — the map feature variability — and use it to weight the output covariance of the proposed algorithm. The effectiveness and robustness of the proposed algorithm are demonstrated using a navigation scenario involving gravitational map matching.

Розроблення схеми та удосконалення методу управління рухом групи мобільних робототехнічних платформ

When managing a group of mobile robotic platforms, there are specific tasks of ensuring operational analysis and taking into account changes in the functioning parameters of each individual platform and the impact of the surrounding environment on it and the group as a whole. It is necessary to realize not only the coordinated management of a separate robotic platform but also to ensure the interaction of separate platforms in order to fulfill the task as a whole. At the same time, it is necessary to analyze the navigational state of the surrounding environment, the composition and coordinates of the platforms in the group, to keep track of the available resources necessary for the performed task. When performing complex tasks by a group of robots, it is necessary to take into account the possibility of losing individual robot during the execution of the task and the fact that each individual robot can perform relatively simple operations, which are determined by its characteristics (radius of action, energy resource, set of executive devices). Groups of mobile robotic platforms can be homogeneous or heterogeneous, which determines the peculiarities of their management. The hybrid management method, which is a combination of centralized and distributed, in the case of heterogeneous platforms, which is most often encountered in practice, should be considered the most adequate. Under the conditions of heterogeneity of platforms in the group, control algorithms should be implemented with unconditional consideration of the features and characteristics of each individual platform. The main requirements for the hybrid management of the robots group are to ensure: effective management of the robots group in real time; respond to changes in working conditions and the surrounding environment; implement various scenarios for achieving a common goal and fulfilling a common task; scaling the number of robots that need to be managed in the group; increasing the accuracy of movement control of each robot in the group. To implement the specified tasks, the method of controlling the movement of a group of mobile robotic platforms has been improved, which, by taking into account the changing parameters of the platforms and the changing state of the surrounding environment, provides effective management of the group of platforms in real time. A generalized scheme of the group management process has been developed, which ensures the adaptation of the group management process to the changing conditions of the surrounding environment. A block diagram of the autonomous motion control algorithm of a separate mobile robotic platform has been developed, which ensures its effective functioning taking into account the variable characteristics of the platform and the state of the environment.

An interpretable knowledge-based decision support method for ship collision avoidance using AIS data

AIS data include ship spatial-temporal and motion parameters which can be used to excavate the deep-seated information. In this article, an interpretable knowledge-based decision support method is established to guide the ship to make collision avoidance decisions with good seamanship and ordinary practice of seamen using AIS data. First, AIS data is preprocessed and trajectory reconstructed to restore the ship historical navigation state, and a ship encounter identification model is constructed according to the encounter characteristics; Second, a two-stage collision avoidance behavior extraction algorithm is formed to build a behavior knowledge base, and the scenario similarity model is constructed to measure and match similar scenarios based on ship position, motion tendency and collision risk. Then, the Delaunay Triangulation Network is used to fuse ship trajectories of similar scenario to form the collision avoidance path. Finally, a case study is performed using the real AIS data outside Ningbo-Zhoushan Port waters, China, and the effectiveness of the planned path is verified by setting the head-on and crossing situations and comparison between the planned and real paths. Results indicate that the proposed model can extract the ship collision avoidance behavior accurately, and the planned path can ensure navigation safety.

A Framework for Collaborative All-Source Navigation With Fault Detection and Exclusion

The ARMAS-SOM framework fuses collaborative all-source sensor information in a resilient manner with fault detection, exclusion, and integrity solutions recognizable to a GNSS user. This framework uses a multifilter residual monitoring approach for fault detection and exclusion, which is augmented with an additional “observability” extended Kalman filter sublayer for resilience. We monitor the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> state covariances in this sublayer to provide intrinsic awareness when navigation state observability assumptions required for integrity are in danger. The framework leverages this to selectively augment with offboard information and preserve resilience. By maintaining split parallel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collaborative</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">proprioceptive</i> instances and employing the “stingy collaboration” technique, we are able to maximize efficient use of network resources, limit the propagation of unknown corruption to a single donor, and maintain consistent collaborative navigation without fear of double-counting in a scalable processing footprint. Last, we preserve the ability to return to autonomy and are able to use the same intrinsic awareness to notify the user when it is safe to do so.

Mission-driven path planning and design of submersible unmanned ship with multiple navigation states

The path planning of unmanned ships at the lowest energy consumption is of great significance for energy savings. At present, the path planning of unmanned ships based on the idea of energy savings is usually oriented toward a single navigation state, and there are few research projects on the path planning of ships that sail across multiple navigation states. The navigation states of submersible unmanned ships of path planning in this paper involves underwater navigation states with various diving depths and water's surface navigation state. The improved genetic algorithm is applied to carry out the path planning for a single navigation state and task-driven multi-navigation states of the submersible unmanned ship at energy-saving velocity and non-energy-saving velocity, respectively. The energy-saving velocities are obtained by establishing optimization models. The results show that, at energy-saving velocity, the energy consumption of the same type of navigation paths increase with the diving depth. There is an obvious velocity demarcation point at non-energy-saving velocity range, which makes the sequence of energy consumption of the three types of paths classed by navigation tasks reversed before and after that point. The research can provide technical support for path planning of cross-domain unmanned ships.

BeamsNet: A data-driven approach enhancing Doppler velocity log measurements for autonomous underwater vehicle navigation

Autonomous underwater vehicles (AUV) perform various applications such as seafloor mapping and underwater structure health monitoring. Commonly, an inertial navigation system aided by a Doppler velocity log (DVL) is used to provide the vehicle’s navigation solution. In such fusion, the DVL provides the velocity vector of the AUV, which determines the navigation solution’s accuracy and helps estimate the navigation states. This paper proposes BeamsNet, an end-to-end deep learning framework to regress the estimated DVL velocity vector that improves the accuracy of the velocity vector estimate, and could replace the model-based approach. Two versions of BeamsNet, differing in their input to the network, are suggested. The first uses the current DVL beam measurements and inertial sensor data, while the other utilizes only DVL data, taking the current and past DVL measurements for the regression process. Both simulation and sea experiments were made to validate the proposed learning approach relative to the model-based approach. Sea experiments were made with the Snapir AUV in the Mediterranean Sea, collecting approximately four and a half hours of DVL and inertial sensor data. Our results show that the proposed approach achieved an improvement of more than 60% in estimating the DVL velocity vector.

Transfer Alignment Technique for Shipboard Missile Strapdown Inertial Navigation System using an Adaptive Kalman Filter

Missile Guidance system needs accurate estimates from Inertial Navigation System (INS) for guiding&#x0D; the vehicle towards the target. In this paper a target point, specified before launch, in a battlefield scenario&#x0D; is considered for a landmark using missile Strap Down Inertial Navigation System (SDINS) aided by Master&#x0D; INS (MINS) placed on a moving platform. Azimuth information of the missile is one of the most critical&#x0D; navigation states for estimation on the moving platform before launching the missile for precise impact.&#x0D; An Adaptive Kalman Filter (AKF) based on the error state model is formulated. The 7-state AKF with 4-measurement forms the core, where the filter gain of the innovation sequence (measurements) is&#x0D; evaluated. This approach of adaptively computing the gain is tested in a laboratory, on a van and in a ship&#x0D; trial, culminating in a successful guided missile launch. Mean and the covariance of the measurement&#x0D; residuals were used in a unique way to compute adaptive gain after the accumulation of initial samples.&#x0D; A Master INS (with advanced Gyros) whose accuracy is much higher than the accuracy of the missile’s&#x0D; SDINS is used for velocity matching algorithm before the launch with execution of an S-maneuver for generation&#x0D; of accelerations towards observing the states more appropriately. Estimated error states were used in a feedback mode to get near the true orientation of the Missile’s slave INS. Error quaternions are used for this purpose in the feedback and the gains were selected using offline matrix Riccati equation solution in a discrete domain as used in the modern control system. The results were very encouraging with less than 5 arc minutes of error in azimuth.

A Multi-Mode Switching Variational Bayesian Adaptive Kalman Filter Algorithm for the SINS/PNS/GMNS Navigation System of Pelagic Ships.

The ocean-going environment is complex and changeable with great uncertainty, which poses a huge challenge to the navigation ability of ships working in the pelagic ocean. In this paper, in an attempt to deal with the complex uncertain interference that the environment may bring to the strap-down inertial navigation system/polarization navigation system/geomagnetic navigation system (SINS/PNS/GMNS) integrated navigation system, the multi-mode switching variational Bayesian adaptive Kalman filter (MMS-VBAKF) algorithm is proposed. To be more specific, to identify the degrees of measurement interference more effectively, we design an interference evaluation and multi-mode switching mechanism using the original polarization information and geomagnetic information. Through this mechanism, the interference to the SINS/PNS/GMNS navigation system is divided into three cases. In case of slight interference (case SI), the variational Bayesian method is adopted directly to solve the problem that the statistical characteristics of measurement noise are unknown. By the fixed-point iteration mechanism, the statistical properties of the measurement noise and the system states can be estimated adaptively in real time. In case of interference-tolerance (case TI), the estimation of the statistical characteristics of measurement noise need to weigh the measurement information at the moment and a priori value information comprehensively. In case of excessive interference (case EI), the SINS/PNS/GMNS integrated navigation system will perform mode switching and filtering system reconstruction in advance. Then, the information fusion and navigation states estimation can be completed. Consequently, the reliability, robustness, and accuracy of the SINS/PNS/GMNS integrated navigation system can be guaranteed. Finally, the effectiveness of the algorithm is illustrated by the simulation experiments.

Variational Bayesian approximation based robust adaptive single beacon navigation method

For the practical application of single beacon navigation system, effective sound velocity (ESV) setting error, measurement outliers and unknown noise statistical parameters are the major sources of positioning error. Although the unknown ESV of single beacon navigation system has been well studied, the other two sources are less concerned. The performance of existing state-of-art navigation methods will deteriorate when there are measurement outliers or noise statistical parameters setting error. This paper aims to fill the gap. We model the process and measurement noise of each state and measurement component based on their own stochastic properties, so that, the influence of measurement outliers can be incorporated into stochastic model. The unknown noise statistical parameters are regarded as random variables and the variational Bayesian approximation method is used to estimate them along with the navigation state. The corresponding robust adaptive single beacon navigation method is proposed. Moreover, the simulation and field data demonstrate that the proposed method is superior to the existing state-of-art navigation methods in the presence of measurement outliers and unknown noise statistical parameters.

Hi/H∞-optimised fault detection for a surface vessel integrated navigation system

AbstractStrapdown inertial navigation systems are widely used in surface ships and warships. Although high-precision optical fibre inertial navigation systems are available, they have high cost and limited practicality. Therefore, they cannot replace the traditional platform inertial navigation systems in all ships. Hence, microelectromechanical system (MEMS)-based inertial sensors are widely used for robust navigation. Accurate and timely identification of sensor faults while ensuring stable navigation is a challenging task. This paper proposes a robust fault detection (FD) approach for a low-cost system that loosely integrates a strapdown inertial navigation system and the global navigation satellite system, where the integrated navigation state estimation provides high-accuracy output. A cubature Hi/H∞-optimised FD filter was designed for a nonlinear discrete time-varying system considering sensitivity to faults and robustness to disturbances. Furthermore, a threshold for FD was derived considering a compromise between the false alarm rate and fault diagnosis accuracy. Finally, the proposed method was validated through simulations using multiple noise distribution sensor data generated by a ship-manoeuvring simulator.

Regulation of Arctic Shipping in Canada and Russia

As Arctic navigation increases and states work, both at the international and the domestic level, at ensuring legal readiness, this article takes a closer look at regulation of Arctic Shipping in Canada and Russia. The analysis first focuses on the current domestic regimes that have developed over the past decades. It highlights that dissimilar political, economic and environmental contexts have shaped not only different shipping patterns off the Canadian and Russian coasts, but also dissimilar coastal state approaches that do not seem to converge noticeably under the influence of the Polar Code. The analysis then turns to challenges that lay ahead as existing regimes could be called into question due to receding ice cover and may need to adapt to evolving shipping patterns and technological innovations.

Reevaluation of Algorithmic Basics for ZUPT-Based Pedestrian Navigation

During the last 10 to 15 years, pedestrian navigation based on zero velocity updates (ZUPT) has become very popular. One of the main reasons for this is the increasing availability of small, low-cost inertial measurement units. However, the processing of the data from these units for pedestrian navigation is almost exclusively based on algorithmic features that originate from classical inertial navigation with high-grade sensors. In addition, the historical background of the ZUPT approach presupposes also sensors with high accuracy. This leads to the problem that neither the ZUPT approach nor the algorithmic features mentioned are consistent with the accuracy level of the inertial measurement units used normally for pedestrian navigation. Therefore, the question arises of whether the usual algorithmic basics and numerical procedures employed by ZUPT-based pedestrian navigation are adequate. Supported by a literature review showing the state of the art, this study investigates the effect of basics such as the system states and the usage of Runge-Kutta method on the navigation accuracy from pedestrian inertial data aided by ZUPT. To this end, a comparative processing of a well-known, published dataset of a short walk and of own data from the authors using different data fusion algorithms was employed. The important results of this study are that the often-used omission of inertial sensor biases cannot be recommended, that a continuous-discrete Kalman filter in combination with a Runge-Kutta algorithm performs better than traditional filter configurations, and that total navigation states are an interesting alternative to classical navigation error-states.

Lie Group Approach to Dynamic-Model-Aided Navigation of Multirotor Unmanned Aerial Vehicles

In this study, the Lie group approach was used for state estimation of the dynamic-model-aided navigation (DMAN) of a small multirotor unmanned aerial vehicle. The unit quaternions constituting a Lie group called the three-sphere space (<i>S</i>&#x00B3;) were used to represent the attitude in the dynamic equations for the process and measurement models. The linearization of these models is presented in terms of Lie algebra corresponding to <i>S</i>&#x00B3;. The use of Lie algebra to describe the attitude increment conforms to the linearity assumption, on which the measurement update of the extended Kalman filter (EKF) is based. In this study, it was experimentally validated that the Lie group approach combined with DMAN performs better than the EKF that uses the conventional linearization of the process and measurement models under the assumption that the nonlinearity effect is negligible for a small attitude increment. It was demonstrated that the navigation states estimated using the proposed model are better than or comparable to those obtained using the current methods, and the proposed method significantly improves the internal properties.

A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filter

Navigation plays an important role in the task execution of the micro-unmanned aerial vehicle (UAV) swarm. The Cooperative Navigation (CN) method that fuses the observation of onboard sensors and relative information between UAVs is a research hotspot. Aiming at the efficiency and accuracy problems of previous studies, this article proposes a hybrid-CN method for UAV swarm based on Factor Graph and Kalman filter. A global Factor Graph is used to combine Global Navigation Satellite System (GNSS) and ranging information to provide position estimations for modifying the distributed Kalman filter; distributed Kalman filter is established on each UAV to fuse inertial information and optimized position estimation to modify the navigation states. In order to provide time-consistent GNSS position information for the Factor Graph, a time synchronization filter is designed. The proposed method is tested and verified using standard Monte Carlo simulations, simulation results show that it can provide a more precise and efficient CN solution than traditional CN methods.

Log-linear Error State Model Derivation without Approximation for INS

Through assembling the navigation parameters as matrix Lie group state, the corresponding inertial navigation system (INS) kinematic model possesses a group-affine property. The Lie logarithm of the navigation state estimation error satisfies a log-linear autonomous differential equation. These log-linear models are still applicable even with arbitrarily large initial errors, which is very attractive for INS initial alignment. However, in existing works, the log-linear models are all derived based on first-order linearization approximation, which seemingly goes against their successful applications in INS initial alignment with large misalignments. In this work, it is shown that the log-linear models can also be derived without any approximation, the error dynamics for both left and right invariant error in continuous time are given in matrix Lie group <inline-formula><tex-math notation="LaTeX">$\mathrm{S}{\mathrm{E}}_2( 3 )$</tex-math></inline-formula> for the first time. This work provides another evidence for the validity of the log-linear model in situations with arbitrarily large initial errors.

Linear Covariance Analysis for Head-On Target Engagements

A linear covariance simulation framework is developed and validated for a two-dimensional target engagement scenario. The scenario comprises a single interceptor equipped with an inertial navigation system aided by absolute position measurements, as well as range, angle, and range rate measurements relative to the incoming target. The interceptor uses a proportional navigation guidance law to engage the target, modeled as nominally constant velocity perturbed by Singer motion accelerations. The linear covariance framework is developed by linearizing the differential and measurement equations about the nominal trajectory, and forming an augmented system comprising truth and navigation state dispersions. In contrast with sample-based methods, the developed linear covariance framework can calculate the truth state dispersion covariance and the estimation error covariance throughout the engagement in a single run. It also provides several advantages over analysis methods such as the adjoint technique or traditional covariance analysis. The linear covariance computational efficiency is exploited to rapidly analyze a head-on target engagement problem.

Closed-Loop Linear Covariance Framework for Path Planning in Static Uncertain Obstacle Fields

Path planning in an uncertain environment is a key enabler of true vehicle autonomy. Over the past two decades, numerous approaches have been developed to account for errors in the vehicle path while navigating complex and often uncertain environments. An important capability of such planning is the prediction of vehicle dispersion covariances about a candidate path. This work develops a new closed-loop linear covariance (CL-LinCov) framework applicable to a wide range of autonomous system architectures. Important features of the developed framework include 1) separation of high-level guidance from low-level control; 2) support for output-feedback controllers with internal states, dynamics, and output; and 3) multi-use continuous sensors for navigation state propagation, guidance, and feedback control. The closed-loop nature of the framework preserves the important coupling between the system dynamics; exogenous disturbances; and the guidance, navigation, and control algorithms. The developed framework is applied to a simplified model of an unmanned aerial vehicle and validated by comparison via Monte Carlo analysis. The utility of the CL-LinCov information is illustrated by its application to path planning in a static, uncertain obstacle field via a modified version of the rapidly exploring random tree algorithm.

Numerical Simulation of Cavitation and Damping Force Characteristics for a High-Speed Supercavitation Vehicle

In this study, an attempt has been made to investigate the supercavitation and hydrodynamic characteristics of high-speed vehicles. A homogeneous equilibrium flow model and a Schnerr–Sauer model based on the Reynolds-averaged Navier–Stokes method are used. Grid-independent inspection and comparison with experimental data in the literature have been carried out to verify the accuracy of numerical methods. The effect of the navigation speed and angle of attack on the cavitation morphology and dynamic characteristics has been investigated. It has been demonstrated that the angle of attack has a remarkable influence on the wet surface and hydrodynamic force, whereas navigation speed has little effect on the position force of the vehicle under the circumstance of no wet surface. The hydrodynamic force changes periodically with the swing of the vehicle, but its maximum is greater than that for the direct navigation state at the same attack angle. Moreover, the damping effect obviously affects the hydrodynamic force amplitude and movement trend.

Autonomous relative navigation error compensation method for small body exploration based on sequence-images

<p indent="0mm">Deep-space small bodies are typical space non-cooperative targets. The autonomous relative navigation of space non-cooperative targets based on sequence-images is an important technical method to realize the small body exploration. Due to the complex on-orbit environment, it is inevitable that the camera will suffer the measurement deviations and the focal length deviation, which will severely affect the relative navigation state estimation accuracy. On the basis of making full use of the information of star sequence images in the field of view of the camera, the initial value of the camera on-orbit calibration is calculated by the least square method. Combined with the measurement deviation state transition model and the starlight angular distance measurement equation between two stars, the measurement deviations are expanded into state estimation parameters and the relative navigation state estimation model based on camera on-orbit error compensation is constructed. Combined with the unscented Kalman filter algorithm, the iterative estimation is carried out through comparing the information between the frames of sequence images. Then the camera measurement deviation and the relative navigation system state are obtained. In this way, the state estimation accuracy is improved. In the simulation analysis, the small body 2016HO3 is considered as the relative navigation target. The simulation results show the effectiveness of the method, which can effectively compensate the camera measurement deviation and achieve the high-precision estimation of the relative navigation system state. The research results provide certain technical support for the subsequent related tasks.

Information Communication Technology (ICT) Tools for Preservation of Underwater Environment: A Vision-Based Posidonia Oceanica Monitoring

Abstract Underwater monitoring activities are crucial for the preservation of marine ecosystems. Currently, scuba divers are involved in data collection campaigns that are repetitive, dangerous, and expensive. This article describes the application of Information Communication Technology (ICT) tools to underwater visual data for monitoring purposes. The data refer to a Posidonia Oceanica survey mission carried out by a scuba diver using a Smart Dive Scooter equipped with visual acquisition and acoustic localization systems. An acoustic-based strategy for geo-referencing of the optical dataset is reported. It exploits the synchronization between the audio track extracted from a camera and the transponder pings adopted for the acoustic positioning. The positioning measurements are employed within an extended Kalman filter to estimate the diver's path during the mission. A visual odometry algorithm is implemented within the filter to refine the navigation state estimation of the diver with respect to the acoustic positioning only. Moreover, a smoothing step based on the Rauch-Tung-Striebel smoother is applied to further improve the estimated diver's positions. Finally, the article reports the results of two different data processing for monitoring applications. The first one is an image mosaicking obtained by concatenating subsequent frames, whereas the second one refers to a qualitative distribution of the Posidonia Oceanica over the mission area accomplished through an image segmentation process. The two outcomes are plotted over a satellite image of the surveyed area, showing that the proposed process is an effective tool capable of facilitating divers in their monitoring and inspection activities.