Attitude Heading Reference System Research Articles

Pohang canal dataset: A multimodal maritime dataset for autonomous navigation in restricted waters

This paper presents a multimodal maritime dataset and the data collection procedure used to gather it, which aims to facilitate autonomous navigation in restricted water environments. The dataset comprises measurements obtained using various perception and navigation sensors, including a stereo camera, an infrared camera, an omnidirectional camera, three LiDARs, a marine radar, a global positioning system, and an attitude heading reference system. The data were collected along a 7.5-km-long route that includes a narrow canal, inner and outer ports, and near-coastal areas in Pohang, South Korea. The collection was conducted under diverse weather and visual conditions. The dataset and its detailed description are available for free download at https://sites.google.com/view/pohang-canal-dataset.

The wavedrifter: a low-cost IMU-based Lagrangian drifter to observe steepening and overturning of surface gravity waves and the transition to turbulence

ABSTRACT Waves and wave breaking are important to many deep and shallow water processes. We describe the wavedrifter, an in situ water-following inertial measurement unit (IMU)-based drifter that measures wave steepening and overturning kinematics, and the subsequent transition to turbulence. The wavedrifter has 5 cm diameter, 77 g mass, and 0.84 saltwater specific gravity. GPS provides time synchronization. MATLAB’s Attitude, Heading, Reference System (AHRS) library provides wavedrifter orientation. Laboratory experiments quantify the wavedrifter vertical oscillation mode, water following properties, and ability to reproduce wave spectra for small, f = 1.5 Hz waves. The wavedrifter observed wave overturning and the transition to turbulence at the Surf Ranch wave basin. Synchronized video observations provide context. The upper-back of the overturn had large ( ≈ 4 g ) accelerations and 14 g acceleration magnitude occurs with the impact of the overturning jet. Trajectories reveal the Lagrangian structure of the overturn and subsurface vortex. Although it has limitations, the wavedrifter is a powerful in situ tool to probe wave processes.

Study of a solid-state vibrating gyroscope in the mode of determining the true azimuth of a given direction and the latitude of the location of the object

The accuracy of determining navigation parameters, the true azimuth of the specified direction, and the latitude of the object's location is being investigated using a Coriolis vibratory gyroscope with a cylindrical metal resonator as a sensitive element of the tactical accuracy class for stabilization systems and control of moving objects. The study used a scheme of a scanning single-component analytical gyroscope compass with the ability to provide vertical direction scanning during the input axis of the angular velocity sensor – the Coriolis vibratory gyroscope. The obtained root-mean-square value of 0,32°·secφ of the error in determining the true azimuth of the specified direction under conditions close to normal confirms the potential possibility of using a Coriolis vibratory gyroscope with a cylindrical metal resonator in orientation and attitude heading reference systems. The average error of 0,34 in determining the latitude of the object's location obtained under the same conditions, with a root-mean-square value of of it is scatter, correlates with the expected result.

Study on attitude heading reference system for an Unmanned Aerial Vehicle in an aerial surveillance scenario

Study on attitude heading reference system for an Unmanned Aerial Vehicle in an aerial surveillance scenario

Magnetometer heading estimation through online calibration for land navigation applications

For land navigation applications, the integration of the magnetometer with the combination of MEMS-INS and the Global Navigation Satellite System (GNSS) give excellent results. During land navigation applications, the magnetometer’s heading can also be used during the GNSS outages. The calibration of the magnetometer is indispensable to calculate its accurate heading. There exist several methods for magnetometer calibration. Some are offline and some are online calibration techniques. In this paper, a calibration method is proposed to estimate the magnetometer’s parameters through online calibration in run time. In this method, the reference magnetic field is calculated from the World Magnetic Model (WMM-2020). Moreover, reference roll, pitch and heading are provided from some other sources such as GNSS, Attitude Heading Reference System (AHRS), or reference INS. For different roll and pitch sectors, calibration parameters are estimated and stored. These parameters are used for magnetometer online calibration during the field testing. Both the headings obtained by the online calibration and conventional lab calibrations are analysed. Furthermore, the heading estimated through the online calibration is autonomous and fast. Subsequently, there is no user involvement in this online calibration technique and no specific movements to the device are provided. The heading obtained by novel technique is as accurate as obtained by conventional offline lab calibration.

Research on the Control Method of Unmanned Helicopter Under the Background of Artificial Intelligence

A synopsis of a multidisciplinary research initiative focused on critical strategies for the genuine independent flight of tiny, vertical flying take-off (VTOL) unmanned aerial vehicles (UAVs). The research activities are the flight testbed, a simulation and test environment, and integrated components for onboard navigation, perception, design, and control. The necessity to create an unmanned helicopter system in different new civil applications cannot be overlooked. A highly reliable model may be used in the design, analysis, and implementation. The helicopter is fitted with a reference system for flight test data measurement and recording attitude heading reference system (AHRS) and the accompanying data storage modules. Recently, artificial intelligence-based deep learning (DL) has demonstrated excellent outcomes for a wide range of robotic activities in the areas of perception, planning, location, and management. Its remarkable skills to learn from complex data obtained in actual surroundings make it appropriate for many autonomous robotic applications. At the same time, UHS is currently widely utilized in various civil tasks in security, cinematography, disaster assistance, package delivery, or warehouse management (Unmanned Helicopter System). This paper conducted detailed work on current applications and the most significant advances and their performance and limits for the DL-UHS method. Furthermore, the essential strategies for deep learning are explained in depth — finally, discussing the principal hurdles of applying deep learning for UHS solutions. The proposed DL-UHS enhance outcome to evaluate the control strategies for the unmanned helicopter to achieve the low signal to noise error ratio of 31.3%, the error rate of 33.6%, the high-performance ratio of 91.4%, enhance accurate path planning 97.5%, prediction ratio of 96.3%, less trajectory cost ratio of 17.8% and increased safety tracking rate 93.6% when compared to other popular methods.

Danger detection technology based on multimodal and multilog data for public safety services

Recently, public safety services have attracted significant attention for their ability to protect people from crimes. Rapid detection of dangerous situations (that is, abnormal situations where someone may be harmed or killed) is required in public safety services to reduce the time required to respond to such situations. This study proposes a novel danger detection technology based on multimodal data, which includes data from multiple sensors (for example, accelerometer, gyroscope, heart rate, air pressure, and global positioning system sensors), and multilog data, which includes contextual logs of humans and places (for example, contextual logs of human activities and crime-ridden districts) over time. To recognize human activity (for example, walk, sit, and punch), the proposed technology uses multimodal data analysis with an attitude heading reference system and long short-term memory. The proposed technology also includes multilog data analysis for detecting whether recognized activities of humans are dangerous. The proposed danger detection technology will benefit public safety services by improving danger detection capabilities.

KM3NeT Detection Unit Line Fit reconstruction using positioning sensors data

The KM3NeT collaboration is constructing two large neutrino detectors in the Mediterranean Sea: ARCA, located near Sicily and aiming at neutrino astronomy, and ORCA, located near Toulon and designed for neutrino oscillation studies. The two detectors, together, will have hundreds of Detection Units (DUs) with 18 Digital Optical Modules (DOMs) maintained vertical by buoyancy, forming a large 3D optical array for detecting the Cherenkov light produced after the neutrino interactions. To properly reconstruct the direction of the incoming neutrino, the position of the DOMs must be known precisely with an accuracy of less than 10 cm. For this purpose, there are acoustic and orientation sensors inside the DOMs. An Attitude Heading Reference System (AHRS) chip provides the components values of the Acceleration and Magnetic field in the DOM, from which it is possible to calculate Yaw, Pitch and Roll for each floor of the line. A piezo sensor detects the signals from fixed acoustic emitters on the sea floor, so as to position it by trilateration. Data from these sensors are used as an input to reconstruct the shape of the entire line based on a DU Line Fit mechanical model. This proceeding presents an overview of the KM3NeT monitoring system, as well as the line fit model and a selection of results.

The Use of Seton as a Bridge to Definitive Ligation of the Intersphincteric Fistula Tract Procedure for Fistula-in-ano: A Systematic Review and Meta-analysis

Abstract Background There is still controversy over the usefulness of seton placement prior to the ligation of the intersphincteric fistula tract (LIFT) surgery in the management of anal fistula. Objective To evaluate the impact of preoperative seton placement on the outcomes of LIFT surgery for the management of fistula-in-ano. Design systematic review and meta-analysis. Data Sources A search was performed on the MEDLINE (PubMed), EMBASE, Scopus, Web of Science, Cochrane Library and Google Scholar databases. Study Selection Original studies without language restriction reporting the primary healing rates with and without seton placement as a bridge to definitive LIFT surgery were included. Intervention The intervention assessed was the LIFT with and without prior seton placement. Main Outcome Measures The main outcome was defined as the primary healing rate with and without the use of seton as a bridge to definitive LIFT surgery. Results Ten studies met the criteria for systematic review, all retrospective, with a pooled study population of 772 patients. There were no significant differences in the percentages of recurrence between patients with and without seton placement (odds ratio [OR] 1.02; 95% confidence interval [CI] 0.73–1.43: p = 0.35). The I2 value was 9%, which shows the homogeneity of the results among the analyzed studies. The 10 included studies demonstrated a weighted average overall recurrence of 38% (interquartile range [IQR] 27–42.7%), recurrence with the use of seton was 40% (IQR 26.6–51.2%), and without its use, the recurrence rate was 51.3% (IQR 31.3–51.3%) Limitations The levels of evidence found in the available literature were relatively fair, as indicated after qualitative evaluation using the Newcastle-Ottawa scale and the Attitude Heading Reference System (AHRS) evidence levels. Conclusions Our meta-analysis suggests that the placement of seton as a bridge treatment prior to LIFT surgery does not significantly improve long-term anal fistula healing outcomes. Ligation of the intersphincteric fistula tract surgery can be performed safely and effectively with no previous seton placement.International prospective register of systematic reviews—PROSPERO registration number: CDR42020149173.

An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning

In smartphone indoor positioning, owing to the strong complementarity between pedestrian dead reckoning (PDR) and WiFi, a hybrid fusion scheme of them is drawing more and more attention. However, the outlier of WiFi will easily degrade the performance of the scheme, to remove them, many researches have been proposed such as: improving the WiFi individually or enhancing the scheme. Nevertheless, due to the inherent received signal strength (RSS) variation, there still exist some unremoved outliers. To solve this problem, this paper proposes the first outlier detection and removal strategy with the aid of Machine Learning (ML), so called WiFi-AGNES (Agglomerative Nesting), based on the extracted positioning characteristics of WiFi when the pedestrian is static. Then, the paper proposes the second outlier detection and removal strategy, so called WiFi-Chain, based on the extracted positioning characteristics of WiFi, PDR, and their complementary characteristics when the pedestrian is walking. Finally, a hybrid fusion scheme is proposed, which integrates the two proposed strategies, WiFi, PDR with an inertial-navigation-system-based (INS-based) attitude heading reference system (AHRS) via Extended Kalman Filter (EKF), and an Unscented Kalman Filter (UKF). The experiment results show that the two proposed strategies are effective and robust. With WiFi-AGNES, the minimum percentage of the maximum error (MaxE) is reduced by 66.5%; with WiFi-Chain, the MaxE of WiFi is less than 4.3 m; further the proposed scheme achieves the best performance, where the root mean square error (RMSE) is 1.43 m. Moreover, since characteristics are universal, the proposed scheme integrated the two characteristic-based strategies also possesses strong robustness.

Design and experimental evaluation of block-pulse functions and Legendre polynomials observer for attitude-heading reference system

The main purpose of this paper is design and implementation of a new linear observer for an attitude and heading reference system (AHRS), which includes three-axis accelerometers, gyroscopes, and magnetometers in the presence of sensors and modeling uncertainties. Since the increase of errors over time is the main difficulty of low-cost micro electro mechanical systems (MEMS) sensors producing instable on–off bias, scale factor (SF), nonlinearity and random walk errors, development of a high-precision observer to improve the accuracy of MEMS-based navigation systems is considered. First, the duality between controller and estimator in a linear system is presented as the base of design method. Next, Legendre polynomials together with block-pulse functions are applied for the solution of a common linear time-varying control problem. Through the duality theory, the obtained control solution results in the block-pulse functions and Legendre polynomials observer (BPLPO). According to product properties of the hybrid functions in addition to the operational matrices of integration, the optimal control problem is simplified to some algebraic equations which particularly fit with low-cost implementations. The improved performance of the MEMS AHRS owing to implementation of BPLPO has been assessed through vehicle field tests in urban area compared with the extended Kalman filter (EKF).

Underwater navigation with 2D forward looking SONAR: An adaptive unscented Kalman filter‐based strategy for AUVs

AbstractOne of the most significant challenges in the underwater domain is to retrieve the autonomous underwater vehicle (AUV) position within the surrounding environment. Indeed, reliable navigation systems are fundamental to perform complex tasks and missions. Most of the navigation filters for AUVs are based on Bayesian estimators such as the linear Kalman Filter (KF), the extended KF, the unscented KF, or the particle filter where, usually, different instruments including a Doppler velocity log (DVL) contribute to the localization task. The usage of forward‐looking SONARs (FLS) in navigation‐aiding is, most of the time, devoted to limiting the navigation drift of the AUV by using simultaneous localization and mapping methods. Therefore, these devices are commonly employed with a standard navigation sensors set comprising an attitude heading reference system and a DVL. In this contribution, the authors propose a novel navigation strategy specifically tailored to AUVs based on an adaptive unscented KF, where linear speed estimations are obtained with a 2D FLS instead of with a DVL and therefore promoting the employment of FLSs as an aid for underwater navigation. The marine robotics community could gain significant benefits from reliable navigation achieved with an FLS‐based navigation architecture. Most importantly, a single FLS can be used for imaging‐related applications (i.e., sonograms acquisition) and navigation, where, instead, different dedicated devices are currently employed for the two tasks. Smaller AUVs usually possess reduced payload carrying capabilities; thus, multitasking use of onboard sensors, which leads to compactness, is a desirable feature. Navigation data obtained during sea trials performed in La Spezia (Italy) at the NATO STO Centre for Maritime Research and Experimentation has been used for offline validation. Afterward, the online results of real autonomous underwater missions undertaken in La Spezia (Italy) and at Vulcano Island, Messina (Italy), are reported.

Attitude heading reference algorithm based on transformed cubature Kalman filter

Stable and accurate attitude estimation is the key to the autonomous control of unmanned aerial vehicle. The Attitude Heading Reference System using micro-electro-mechanical system inertial measurement unit and magnetic sensor as measurement sensors is an indispensable system for attitude estimation of the unmanned aerial vehicle. Aiming at the problem of low precision of the Attitude Heading Reference System caused by the nonlinear attitude model of the micro unmanned aerial vehicle, an attitude heading reference algorithm based on cubature Kalman filter is proposed. Aiming at the nonlocal sampling problem of cubature Kalman filter, the transformed cubature Kalman filter using orthogonal transformation of the sampling point is presented. Meanwhile, an adaptive estimation algorithm of motion acceleration using Kalman filter is proposed, which realizes the online estimation of motion acceleration. The car-based tests show that the algorithm proposed in this paper can accurately estimate the carrier’s motion attitude and motion acceleration without global positioning system. The accuracy of acceleration reaches 0.2 m/s2, and the accuracy of attitude reaches 1°.

INDIRECT CONTROL GYROCOMPASS FOR LAND MOBILE VEHICLES

This work consists of two parts. The first part is a historical overview of the creation of two generations of marine and land gyrocompasses with direct and indirect control. During the review of pendulum or direct control gyrocompasses, using primary sources and authoritative secondary sources, the main practical and analytical developments that led to the creation of the first industrially applicable marine gyrocompass are highlighted. The review of non pendulum or indirect control gyrocompasses is largely an original study of the origins of their creation that is provided through an analysis of available patents taking into account the lack of other available early sources. There are distinguished the developments according to the patent of Narciss Ach (1906, Germany) where the scheme of the gyrocompass with indirect control probably appeared for the first time, the developments according to the patent of Preston R. Basset (1923, USA), where the scheme of proper tuning of indirect control gyrocompass appeared for the first time, and other developments as well. There are also considered the developments of a gyrocompass with electromagnetic correction by Pavel Koptyaev (1932, 1951-1954, USSR) and the main industrial developments of corrected gyrocompasses in the territory of the former USSR. The review also briefly indicates the main operational properties of marine and land gyrocompasses, including a new generation of gyrocompasses based on fiber-optic gyroscopes (FOG) and hemispherical resonator gyroscopes (HRG), attitude heading reference system (AHRS). The second part of this work reasonably follows from the first part, in which the gyrocompass with indirect control for land mobile vehicles based on a dynamically tuned gyroscope (DTG) on a platform stabilized in the horizon plane is studied. A general model of errors of such gyrocompass is determined, when the main axis of the gyroscope is brought into the plane of the true (geographical) meridian in the direction to the North and in the direction to the South. A hardware compensation scheme for the systematic errors of the gyrocompass is provided. A problem of determining and compensating for the gyroscope own drift relative to the horizontal axis of the suspension is solved through indirect measurements. The estimation of parameters of the indirect control gyrocompass is provided as a device of a navigation accuracy class

In-Motion Alignment for a Velocity-Aided SINS With Latitude Uncertainty

For situations where accurate attitude information is needed while the position is not readily available or not necessarily required, such as the attitude heading reference system, this article proposes a velocity-aided in-motion alignment scheme for a strapdown inertial navigation system (SINS) during geographic latitude uncertainty. In the coarse alignment stage, a gravitational apparent motion rebuilding method based on the integral velocity smoothing is designed to roughly determine the attitude and latitude information. Then, the SINS/velocity-integrated navigation method uses the recorded data to complete the fine alignment. Due to the adoption of a well-designed backtracking navigation strategy, the storage space and computation load are reduced to the degree that an ordinary navigation computer can work with. Nine ship tests were carried out and the results show that the proposed method can align the azimuth with an accuracy of 0.04° in a moving environment, which is the same as that of normal in-motion alignment with a known position.

A Nonlinear Double Model for Multisensor-Integrated Navigation Using the Federated EKF Algorithm for Small UAVs.

Aimed at improving upon the disadvantages of the single centralized Kalman filter for integrated navigation, including its fragile robustness and low solution accuracy, a nonlinear double model based on the improved decentralized federated extended Kalman filter (EKF) for integrated navigation is proposed. The multisensor error model is established and simplified in this paper according to the near-ground short distance navigation applications of small unmanned aerial vehicles (UAVs). In order to overcome the centralized Kalman filter that is used in the linear Gaussian system, the improved federated EKF is designed for multisensor-integrated navigation. Subsequently, because of the navigation requirements of UAVs, especially for the attitude solution accuracy, this paper presents a nonlinear double model that consists of the nonlinear attitude heading reference system (AHRS) model and nonlinear strapdown inertial navigation system (SINS)/GPS-integrated navigation model. Moreover, the common state parameters of the nonlinear double model are optimized by the federated filter to obtain a better attitude. The proposed algorithm is compared with multisensor complementary filtering (MSCF) and multisensor EKF (MSEKF) using collected flight sensors data. The simulation and experimental tests demonstrate that the proposed algorithm has a good robustness and state estimation solution accuracy.

Onboard Visual Horizon Detection for Unmanned Aerial Systems with Programmable Logic

We introduce and analyze a fast horizon detection algorithm with native radial distortion handling and its implementation on a low power field programmable gate array (FPGA) development board in this paper. The algorithm is suited for visual applications in an airborne environment, that is on board a small unmanned aircraft. The algorithm was designed to have low complexity because of the power consumption requirements. To keep the computational cost low, an initial guess for the horizon is used, which is provided by the attitude heading reference system of the aircraft. The camera model takes radial distortions into account, which is necessary for a wide-angle lens used in most applications. This paper presents formulae for distorted horizon lines and a gradient sampling-based resolution-independent single shot algorithm for finding a horizon with radial distortion without undistortion of the complete image. The implemented algorithm is part of our visual sense-and-avoid system, where it is used for the sky-ground separation, and the performance of the algorithm is tested on real flight data. The FPGA implementation of the horizon detection method makes it possible to add this efficient module to any FPGA-based vision system.

Derived Angle of Attack and Sideslip Angle Characterization for General Aviation

Proper exploitation of derived angle of attack (AOA) and sideslip angle from low-cost attitude heading reference system sensors found in general aviation aircraft is a candidate solution for improving flight safety. This approach uses equations for AOA and sideslip angle that are solved on board in real-time, using state data and information provided by sensors. These equations are a function of vehicle parameters and stability and control derivatives, in addition to the angular displacement and angular rate sensor outputs. This paper characterizes and evaluates the feasibility of derived AOA and sideslip angle for part 23 use cases of pilot displays, envelope protection, and fly-by-wire flight control systems. Standard AOA and sideslip angle equations and sensor models are implemented in a nonlinear six-degree-of-freedom simulation model of the Cessna 172. Piloted simulation inputs are used in a non-real-time Monte Carlo simulation to evaluate the effect of modeling uncertainties and sensor noise on derived AOA and sideslip angle. Results presented in the paper show the allowable bounds on aircraft model parametric uncertainties such as stability and control derivatives, in addition to sensor noise levels, that produce usable derived AOA and sideslip angle values.

Adaptive Attitude Estimation for Low-Cost MEMS IMU Using Ellipsoidal Method

In this article, the attitude estimation for low-cost MEMS inertial measurement units in a smartphone is proposed using adaptive ellipsoidal methods. Accelerometer and magnetometer measurements in an attitude heading reference system (AHRS) are ideally applicable when there is no acceleration and magnetic disturbance. However, when the AHRS is applied to the smartphone, acceleration is frequently occurred by hand movement, and it causes significant attitude errors. Besides, magnetic disturbance from the surrounding environment degrades the heading estimation. In order to improve the attitude accuracy, the acceleration from movement, in addition to the magnetic disturbance is considered in adaptive logic using measurement residuals in this article. The performance of the proposed algorithm is simulated by the computer and tested using the rate table and optical motion capture system compared with the conventional algorithms.

MEMS-Based IMU Drift Minimization: Sage Husa Adaptive Robust Kalman Filtering

The Attitude Heading Reference System (AHRS) has been widely used to provide the position and orientation of a rigid body. A low cost MEMS based inertial sensor measurement unit (IMU) is a core device in AHRS. To improve the AHRS system performance, there is a need to develop (i) stochastic IMU error models and (ii) random noise minimization techniques. In this paper, we modify the Sage-Husa Adaptive Kalman Filter (SHAKF) to incorporate time-varying noise estimator and robustifier, termed as Modified Sage-Husa Adaptive Robust Kalman Filter (MSHARKF). In the proposed algorithm, a three segment approach is used to evaluate the adaptive scale factor followed by the learning statistics. The scale factor is iteratively updated in the MSHARKF equations. In addition, angle random walk (ARW) and bias instability (BI) errors are represented by state-space models. The proposed algorithm is applied to restrain the drift error and random noise in the MEMS IMUs signals. The performance of this algorithm is analyzed using Allan variance (AV) analysis for static signals whereas the Root Mean Square Error (RMSE) values are evaluated for dynamic signals. Experimental results demonstrate the effectiveness of MSHARKF in reducing the drift and random noise in static and dynamic conditions as compared with other existing algorithms. Finally, we present sufficient conditions for convergence proof of the MSHARKF algorithm.