Improve Navigation Accuracy Research Articles

A novel X-ray pulsar integrated navigation method for ballistic aircraft

Since X-ray pulsar navigation system (XNAV) has better anti-interference and autonomy, a novel X-ray pulsar integrated navigation method combining celestial navigation system (CNS) and strapdown inertial navigation system (SINS) is proposed in this paper. Using XNAV and CNS to simultaneously correct the output of SINS not only solves the problem of error accumulation in SINS, but also ensures that the system has a high degree of autonomy and anti-interference. In order to further improve navigation accuracy, a suboptimal multiple fading extended Kalman filter (SMFEKF) is used for information fusion between XNAV and the other two navigation methods. Finally, numerical simulations show that the integrated navigation algorithm not only has higher accuracy than SINS and CNS/SINS, but also can output attitude information that XNAV cannot output. And navigation accuracy increases with the number of pulsars. Besides, the accuracy of XNAV/CNS/SINS using SMFEKF is significantly better than EKF.

Odometer and MEMS IMU enhancing PPP under weak satellite observability environments

Accuracy of dynamic precise point positioning (PPP) degrades significantly under users’ challenging environments because of the limited or unavailable GPS observations. To overcome such weakness, a tight-integration system of micro-electromechanical systems (MEMS) inertial measurement unit (IMU), odometer, and GPS ionospheric delay constrained PPP is investigated to provide users positions and attitudes with higher accuracy, reliability, and continuity under the weak GPS observability environments. In this approach, the data from different sensors are integrated in two joint extend Kalman filters (EKF). One is for PPP/INS tight-integration and the other one is for odometer aiding the solutions from INS or PPP/INS tight-integration modes. Here, the advantages of slow time-varying ionospheric delay, high-accuracy in short time of inertial navigation system (INS), and hardly-disturbed odometer are utilized effectively to enhance PPP solutions. The corresponding mathematical models are provided and are evaluated by a set of one-hour real land-borne test data collected from a MEMS IMU, an odometer, and a dual-frequency GPS receiver in suburbs of Wuhan city, China, and also a set of simulated weak satellites availability data which is to imitate the GPS availability under unban canyons. Results indicate that the fusion system can improve navigation accuracy of GPS or GPS/INS significantly. That are, more than fifty percentages position enhancements in north-east-down components, and about forty percentages attitude enhancements in roll-pitch-yaw direction under the weak GPS availability.

X‐ray pulsar navigation using multiple detectors based on a new observation strategy

Pulsar navigation has gradually attracted increased attention because of its unique advantages in the study of high-attitude missions and deep space exploration. Low navigation accuracy, due to the long integration period, is one of the main problems with X-ray pulsar navigation. In this study, a new observation strategy for pulsar navigation that could greatly increase observations compared with the traditional synchronous observation model has been proposed. Due to the uncertainty of the measured angles of the pulsar navigation model, a robust Kalman filter was designed based on the new observation model, and mathematical simulation results showed that the proposed method had improved navigation accuracy.

An adaptive deep-coupled GNSS/INS navigation system with hybrid pre-filter processing

The deep-coupling of a global navigation satellite system (GNSS) with an inertial navigation system (INS) can provide accurate and reliable navigation information. There are several kinds of deeply-coupled structures. These can be divided mainly into coherent and non-coherent pre-filter based structures, which have their own strong advantages and disadvantages, especially in accuracy and robustness. In this paper, the existing pre-filters of the deeply-coupled structures are analyzed and modified to improve them firstly. Then, an adaptive GNSS/INS deeply-coupled algorithm with hybrid pre-filters processing is proposed to combine the advantages of coherent and non-coherent structures. An adaptive hysteresis controller is designed to implement the hybrid pre-filters processing strategy. The simulation and vehicle test results show that the adaptive deeply-coupled algorithm with hybrid pre-filters processing can effectively improve navigation accuracy and robustness, especially in a GNSS-challenged environment.

UAV State Estimation Modeling Techniques in AHRS

Autonomous unmanned aerial vehicle (UAV) system is depending on state estimation feedback to control flight operation. Estimation on the correct state improves navigation accuracy and achieves flight mission safely. One of the sensors configuration used in UAV state is Attitude Heading and Reference System (AHRS) with application of Extended Kalman Filter (EKF) or feedback controller. The results of these two different techniques in estimating UAV states in AHRS configuration are displayed through position and attitude graphs.

An IMM-Aided ZUPT Methodology for an INS/DVL Integrated Navigation System.

Inertial navigation system (INS)/Doppler velocity log (DVL) integration is the most common navigation solution for underwater vehicles. Due to the complex underwater environment, the velocity information provided by DVL always contains some errors. To improve navigation accuracy, zero velocity update (ZUPT) technology is considered, which is an effective algorithm for land vehicles to mitigate the navigation error during the pure INS mode. However, in contrast to ground vehicles, the ZUPT solution cannot be used directly for underwater vehicles because of the existence of the water current. In order to leverage the strengths of the ZUPT method and the INS/DVL solution, an interactive multiple model (IMM)-aided ZUPT methodology for the INS/DVL-integrated underwater navigation system is proposed. Both the INS/DVL and INS/ZUPT models are constructed and operated in parallel, with weights calculated according to their innovations and innovation covariance matrices. Simulations are conducted to evaluate the proposed algorithm. The results indicate that the IMM-aided ZUPT solution outperforms both the INS/DVL solution and the INS/ZUPT solution in the underwater environment, which can properly distinguish between the ZUPT and non-ZUPT conditions. In addition, during DVL outage, the effectiveness of the proposed algorithm is also verified.

Navigation accuracy after automatic- and hybrid-surface registration in sinus and skull base surgery.

ObjectiveComputer-aided-surgery in ENT surgery is mainly used for sinus surgery but navigation accuracy still reaches its limits for skull base procedures. Knowledge of navigation accuracy in distinct anatomical regions is therefore mandatory. This study examined whether navigation accuracy can be improved in specific anatomical localizations by using hybrid registration technique.Study designExperimental phantom study.SettingOperating room.Subjects and methodsThe gold standard of screw registration was compared with automatic LED-mask-registration alone, and in combination with additional surface matching. 3D-printer-based skull models with individual fabricated silicone skin were used for the experiments. Overall navigation accuracy considering 26 target fiducials distributed over each skull was measured as well as the accuracy on selected anatomic localizations.ResultsOverall navigation accuracy was <1.0 mm in all cases, showing the significantly lowest values after screw registration (0.66 ± 0.08 mm), followed by hybrid registration (0.83± 0.08 mm), and sole mask registration (0.92 ± 0.13 mm).On selected anatomic localizations screw registration was significantly superior on the sphenoid sinus and on the internal auditory canal. However, mask registration showed significantly better accuracy results on the midface. Navigation accuracy on skull base localizations could be significantly improved by the combination of mask registration and additional surface matching.ConclusionOverall navigation accuracy gives no sufficient information regarding navigation accuracy in a distinct anatomic area. The non-invasive LED-mask-registration proved to be an alternative in clinical routine showing best accuracy results on the midface. For challenging skull base procedures a hybrid registration technique is recommendable which improves navigation accuracy significantly in this operating field. Invasive registration procedures are reserved for selected challenging skull base operations where the required high precision warrants the invasiveness.

Polar Grid Navigation Algorithm for Unmanned Underwater Vehicles.

To solve the unavailability of a traditional strapdown inertial navigation system (SINS) for unmanned underwater vehicles (UUVs) in the polar region, a polar grid navigation algorithm for UUVs is proposed in this paper. Precise navigation is the basis for UUVs to complete missions. The rapid convergence of Earth meridians and the serious polar environment make it difficult to establish the true heading of the UUV at a particular instant. Traditional SINS and traditional representation of position are not suitable in the polar region. Due to the restrictions of the complex underwater conditions in the polar region, a SINS based on the grid frame with the assistance of the OCTANS and the Doppler velocity log (DVL) is chosen for a UUV navigating in the polar region. Data fusion of the integrated navigation system is realized by a modified fuzzy adaptive Kalman filter (MFAKF). By neglecting the negative terms, and using T-S fuzzy logic in the adaptive regulation of the noise covariance, the proposed filter algorithm can improve navigation accuracy. Simulation and experimental results demonstrate that the polar grid navigation algorithm can effectively navigate a UUV sailing in the polar region.

An improved gravity compensation method for high-precision free-INS based on MEC–BP–AdaBoost

In recent years, with the rapid improvement of inertial sensors (accelerometers and gyroscopes), gravity compensation has become more important for improving navigation accuracy in inertial navigation systems (INS), especially for high-precision INS. This paper proposes a mind evolutionary computation (MEC) back propagation (BP) AdaBoost algorithm neural-network-based gravity compensation method that estimates the gravity disturbance on the track based on measured gravity data. A MEC–BP–AdaBoost network-based gravity compensation algorithm used in the training process to establish the prediction model takes the carrier position (longitude and latitude) provided by INS as the input data and the gravity disturbance as the output data, and then compensates the obtained gravity disturbance into the INS’s error equations to restrain the position error propagation. The MEC–BP–AdaBoost algorithm can not only effectively avoid BP neural networks being trapped in local extrema, but also perfectly solve the nonlinearity between the input and output data that cannot be solved by traditional interpolation methods, such as least-square collocation (LSC) interpolation. The accuracy and feasibility of the proposed interpolation method are verified through numerical tests. A comparison of several other compensation methods applied in field experiments, including LSC interpolation and traditional BP interpolation, highlights the superior performance of the proposed method. The field experiment results show that the maximum value of the position error can reduce by 28% with the proposed gravity compensation method.

Multirate Observation-Based X-Ray Pulsar Navigation Technique

AbstractX-ray pulsar navigation techniques can provide the information of position and velocity for spacecraft and have proven to be an extremely promising autonomous navigation solution. Currently, one of the main problems of pulsar navigation is that the update rate of the measurement data is low. In this paper, a multirate observation pulsar navigation model is proposed to improve the update rate of measurement data. To solve the uncertainty of the measured angles in the multirate observation model of pulsar navigation, a robust extended Kalman filter (REKF) is also proposed. In order to verify the effectiveness of the model and filtering algorithm, the root-mean square (RMS) errors of the REKF for multirate observation model is compared with the extended Kalman filter (EKF) for a multirate observation model and REKF for a synchronous observation model. The simulations results show that the proposed multirate observation model and REKF are valid to improve navigation accuracy.

Resolving Temporal Variations in Data-Driven Flow Models Constructed by Motion Tomography

Abstract: Modeling and predicting ocean flow are great challenges in physical oceanography. To answer such challenges, mobile sensing platforms have been an effective tool for providing Lagrangian flow information. Such information is typically incorporated into ocean models using Lagrangian data assimilation which requires significant amount of computing power and time. Motion tomography (MT) constructs generic environmental models (GEMs) that combine computational ocean models with real-time data collected from mobile platforms to provide high-resolution predictions near the mobile platforms. MT employs Lagrangian data from mobile platforms to create a spatial map of flow in the region traversed by the mobile platforms. This paper extends the MT method to resolve the coupling between temporal variations and spatial variations in flow modeling. Along with Lagrangian data from a mobile sensor, Eulerian data are collected from a stationary sensor deployed in the region where the mobile sensor collects data. Assimilation of these two data sets into GEMs introduces a nonlinear filtering problem. This paper presents the formulation of such nonlinear filtering problem and derives a filtering method for estimating flow model parameters. We analyze observability for the derived filters and demonstrate that the resulting method improves navigation accuracy for mobile platforms.

Learning Uncertainty in Ocean Current Predictions for Safe and Reliable Navigation of Underwater Vehicles

Operating autonomous underwater vehicles AUVs near shore is challenging-heavy shipping traffic and other hazards threaten AUV safety at the surface, and strong ocean currents impede navigation when underwater. Predictive models of ocean currents have been shown to improve navigation accuracy, but these forecasts are typically noisy, making it challenging to use them effectively. Prior work has explored the use of probabilistic planners, such as Markov decision processes MDPs, for planning in these scenarios, but prior methods have lacked a principled way of modeling the uncertainty in ocean model predictions, which limits applicability to cases in which high fidelity models are available. To overcome this limitation, we propose using Gaussian processes GPs augmented with interpolation variance to provide confidence measures on predictions. This paper describes two novel planners that incorporate these confidence measures: 1 a stationary risk-aware GPMDP for low-variability currents, and 2 a nonstationary risk-aware NS-GPMDP for faster and high-variability currents. Extensive simulations indicate that the learned confidence measures allow for safe and reliable operation with uncertain ocean current models. Field tests of the planners on Slocum gliders over several weeks in the ocean demonstrate the practical efficacy of our approach.

Integrated Autonomous Navigation System and Automatic Large Scale Three Dimensional Map Construction

&lt;div class=""abs_img""&gt; &lt;img src=""[disp_template_path]/JRM/abst-image/00270004/10.jpg"" width=""300"" /&gt; Constructed large-scale 3D map&lt;/div&gt; The method we propose for constructing a large three-dimensional (3D) map uses an autonomous mobile robot whose navigation system enables the map to be constructed. Maps are vital to autonomous navigation, but constructing and updating them while ensuring that they are accurate is challenging because the navigation system usually requires accurate maps. We propose a navigation system that explores areas not explored before. The proposed system mainly uses LIDARs for determining its own position – a process known as localization – or the environment around the robot – a process known as environment recognition – for creating local maps and for avoiding mobile objects – a process known as motion planning. We constructed a detailed 3D map automatically using autonomous driving data to improve navigation accuracy without increasing the operator’s workload, confirming the feasibility of the proposed method through experiments. &lt;/span&gt;

Real-Time Guidance of Underwater Gliders Assisted by Predictive Ocean Models

AbstractIn recent years, collecting scientific data from ocean environments has been increasingly undertaken by underwater gliders. For better navigation performance, the influence of flow on the navigation of underwater gliders may be significantly reduced by estimating flow velocity. However, methods for estimating flow do not always account for spatial and temporal changes in the flow field, leading to poor navigation in complex ocean environments. To improve navigation accuracy in such environmental conditions, this paper studies an approach for the real-time guidance of underwater gliders assisted by predictive ocean models. This study is motivated by glider deployments conducted from January to April 2012 and in February 2013 in Long Bay, South Carolina, where the ocean currents are characterized by strong tides and a stronger alongshore current, the Gulf Stream. The flow speed here often exceeds the forward speed of the glider. To deal with such a challenge, a computationally efficient method of depth-averaged ocean current modeling was developed. The method adjusts the ocean model based on the most recent ocean observations from gliders as feedback, and flow predictions from the model are incorporated into path planning, which produces waypoints. The entire process of flow prediction, path planning, and waypoint computation is performed off-board the gliders in real time by the glider navigation support system, the Glider-Environment Network Information System (GENIoS). This paper presents the setup and method for the glider navigation strategy applied to the Long Bay deployments. For demonstration, the performance of the method described here is compared to that of the default method implemented in the built-in glider navigation system.

Food searches and guiding structures in North African desert ants, Cataglyphis.

North African desert ants, Cataglyphis fortis, use path integration as their primary means of navigation. The ants also use landmarks when these are available to improve navigation accuracy. Extended landmarks, such as walls and channels, may serve further functions, for example, local guidance or triggering of local vectors. The roles of such structures were usually examined in homing animals but not during food searches. When searching for familiar feeding sites, Cataglyphis may show intriguing deviations from expected search performances. These may result from the presence of extended landmarks, namely experimental channels. Here we scrutinise this hypothesis of landmark guidance in food searches. We prevented the ants from seeing the channel walls by covering their eyes, except the dorsal rim area. This experiment was repeated in the open test field with an alley of black cylinders to extend our findings to a more normal foraging environment. Ants with covered eyes did not deviate from expected search performances, whereas ants with normal eyes extended their searches along the axis of the leading structures by 15–20 %, in both channels and landmark alleys. This demonstrates that Cataglyphis orients along extended landmarks when searching for familiar food sources and alters its search pattern accordingly.

Analysis and Improvement of Attitude Output Accuracy in Rotation Inertial Navigation System

Inertial navigation system (INS) measures vehicle’s angular rate and acceleration by orthogonally mounted tri-axis gyroscopes and accelerometers and then calculates the vehicle’s real-time attitude, velocity, and position. Gyroscope drifts and accelerometer biases are the key factors that affect the navigation accuracy. Theoretical analysis and experimental results show that the influence of gyroscope drifts and accelerometer biases can be restrained greatly in rotation INS (RINS) by driving the inertial measurement unit (IMU) rotating regularly, thus improving navigation accuracy significantly. High accuracy in position and velocity should be matched with that in attitude theoretically since INS is based on dead reckoning. However, the marine and vehicle experiments show that short-term attitude output accuracy of RINS is even worse compared with that of nonrotation INS. The loss of attitude accuracy has serious impacts on many task systems where high attitude accuracy is required. This paper researched the principle of attitude output accuracy loss in RINS and then proposed a new attitude output accuracy improvement algorithm for RINS. Experiment results show that the proposed attitude compensation method can improve short-term pitch and roll output accuracy from 20~30 arc seconds to less than 5 arc seconds and azimuth output accuracy improved from 2~3 arc minutes to less than 0.5 arc minutes in RINS.

The Performance Analysis of the Tactical Inertial Navigator Aided by Non-GPS Derived References

The Inertial Navigation System (INS) is now widely applied in many navigation and mobile mapping applications due to its high sampling rates, high accuracy in short-term cases, and no limitations caused by interference or signal obstructions. In addition, the INS can continuously provide the position, velocity and attitude of a vehicle. Conversely, the disadvantage of the stand-alone INS is that its accuracy degrades rapidly with time because of the accumulations of systematic errors and noises from accelerometers and gyroscopes. Therefore, this research aims to implement an integrated system with specific 3D position updates using non-GPS derived references to aid a tactical inertial navigator to provide seamless navigation solutions in the specific area without Global Positioning System (GPS) signals. An Extended Kalman Filter (EKF) is applied as the core estimator to provide superior performance and output the navigation solutions in real-time. The INS is updated by position from references such as the digital map, land mark, Digital Terrain Model (DTM) as well as waypoint to improve navigation accuracy in the long-term. In order to evaluate the performance of the proposed algorithm, field tests including land scenario in freeway and airborne scenario with an unmanned aerial test platform have been conducted. The preliminary results demonstrate that the proposed algorithm with non-GPS derived references aiding from digital map and waypoint for onboard aerial camera trigger to provide uninterrupted navigation solutions and better performance which can achieve the meter-level accuracy without GPS aiding for land and aerial scenarios, respectively.

Improving unmanned ground vehicle navigation by exploiting virtual sensors and multisensor fusion

This paper investigates techniques on improving navigation accuracy using multiple sensors mounted on a mobile platform and exploiting the inherent characteristic of a ground vehicle that does not move along the cross-track and off the ground, often termed nonholonomic constraints (NHC) for car-like vehicles that assume no slip or skid. The forward velocity of the vehicle is obtained using a wheel encoder. The 3D velocity vector becomes observable during the normal moving state of the vehicle by using NHC, which produces one virtual sensor. Another virtual sensor is the zero-velocity update (ZVU) condition of the vehicle; when the condition is true, the 3D velocity vector (which is zero) becomes observable. These observables were employed in an extended Kalman filter (EKF) update to limit the growth of inertial navigation system error. We designed an EKF for data fusion of inertial measurement units, global positioning systems (GPS), and motion constraints (i.e., NHC and ZVU). We analyzed the effects of utilizing these constraints on improving navigation accuracy in stationary and dynamic cases. Our proposed navigation suite provides reliable accuracy for unmanned ground vehicle applications in a GPS-denied environment (e.g., forest canopy and urban canyon).

An innovative high-precision SINS/CNS deep integrated navigation scheme for the Mars rover

The Mars rovers are the key to Mars exploration missions. In order to achieve long-time autonomous roving and perform science operations, Mars rovers need to have the capability of high-precision autonomous navigation. According to the characteristics of the Martian environment, an innovative scheme of strap-down inertial navigation system/celestial navigation system (SINS/CNS) deep integration for the Mars rover and its implementation are proposed. First, the large field of view (FOV) star sensor is utilized to assist the attitude matrix of SINS by continually observing and correcting the misalignment angles and gyro drifts. Thus, the high-precision mathematic reference is obtained. Then, the star sensor makes use of the reference provided by SINS to obtain the local position vector. The rover's position and attitude can be determined by CNS. On the basis of the mathematics reference, the innovative scheme of SINS/CNS deep integration which takes full advantage of the navigation information of each subsystem is presented. Moreover, the errors of inertial measurement unit (IMU) and the navigation parameters in SINS are estimated and corrected. And the deep integration of SINS/CNS is performed. Compared to the optimal integration mode, this scheme improves navigation accuracy. In the end, the simulation results indicate that this scheme is steady and reliable.

포항 로란-C(9930M) 이용 영일만 dLoran 측정

dLoran과 ASF 데이터 맵 그리고 로란 데이터 채널은 eLoran 시스템의 중요한 3 요소이다. dLoran은 eLoran 기술의 핵심 기술로 ASF 보정을 통해 항법 정확도를 향상시키는 기술이다. 이러한 dLoran 보정을 통해 항만 접안(HEA)시에 8~20 m 정확도의 항법 성능을 얻을 수 있다. 본 연구에서는 로란 9930M 체인 중에 주국인 포항 송신국의 신호를 이용하여 dLoran 측정을 하였다. 영일만 해상을 대상으로 dLoran 기준국을 포항 호미곶 표지관리소에 설치하고 시험용 수신기를 흥환 해수욕장에 설치하여 dLoran 측정의 유효성을 평가하였다. 그 결과 표지관리소 dLoran 기준국의 TOA 측정 데이터와 흥환 시험국의 이용자 수신기 TOA 측정 데이터의 하루 동안의 차분 데이터는 약 10~30 ns (거리오차: 3~9 m) 이내로 일치하고 있어서 이 dLoran 측정 데이터로 이용자의 ASF 측정값을 보정하면 eLoran의 항만 접안에서의 항법 정확도를 만족할 수 있다. There are three essential components of eLoran: dLoran, data map of ASF, and the Loran data channel. Particularly, dLoran improves navigation accuracy, which is the core technology of eLoran systems. The requirement of HEA's absolute accuracy, less than 20 meters, can be satisfied via dLoran measurements and their corrections. In this study, dLoran measurements using the Pohang Loran-C (9930M) station signal were conducted at Yeongil Bay. We established a dLoran reference station at Homigot Management Office for navigation aids within the Bay. We estimated the effectiveness of the dLoran between the reference site (Homigot Management Office) and a test site (Heunghwan beach) by measuring TOAs. We verified that the TOA data measured at these two regions were highly correlated. The temporal differences in the data between the dLoran reference station and test site were about 10~30 ns per day, which is equivalent to a ranging error of 3~9 m. This result shows that eLoran can meet the requirement of 8~20 meters position accuracy for maritime HEA by correcting the ASF at the user's receiver.