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Robust Global Navigation Satellite System (GNSS) factors are introduced into a factor graph optimisation based integrated navigation system to address the challenge of occluded GNSS signals during ship navigation, which leads to increased errors in positioning results. To enhance the robustness of the GNSS tracking loop, a vector tracking method is applied to receiver tracking loop. Then, the Mahalanobis distance was employed to assess the pseudorange residual and identify and reject signals that exhibit anomalies. Specifically, the pseudorange residual is computed as the difference between the predicted pseudorange of the GNSS receiver and the measured pseudorange. Using the historical information in the window, robust GNSS factors were constructed for use in the factor graph. The robust factor graph optimisation method for a shipborne GNSS/Inertial Navigation System integrated navigation system was implemented by constructing robust GNSS factors and Inertial Measurement Unit factors. The experimental results confirm that the positioning accuracy of the proposed method is superior to those of the factor graph optimization and extended Kalman filter. [ABSTRACT FROM AUTHOR]

An integrity monitoring of integrated navigation in the presence of slowly growing errors (SGEs) is proposed. SGEs with gradual growth over time are the worst category of latent failures. Tightly coupled inertial navigation system (INS)/Global Navigation Satellite System integration is presented by taking INS errors, bias and drift of receiver clock, baro‐altitude, pseudo‐range errors and ionospheric errors into consideration. Based on the RTCA performance standards DO‐229 and DO‐384, the simplified multi‐filters solution separation is employed for fault detection and calculation of corresponding outputs. For the fault exclusion part, the authors introduce a post pseudo‐range residual and measurement averaging method that is designed to quickly and effectively address SGEs and large measurement noises. Furthermore, the authors have designed a reconstruction logic for the filters that guarantees the continuity of the civil aviation system by considering the variation of visible satellites and historical information. The simulation results demonstrate that the proposed approach satisfies the performance requirements for civil aviation of En‐route and is able to correctly exclude faults and respond quickly in the presence of different levels of SGEs and measurement noises. [ABSTRACT FROM AUTHOR]

3 J. Yang and S. Lee, Ultra-wideband coupled relative positioning algorithm applicable to flight controller for multidrone collaboration, ETRI J. 45 (2023), no. 5. In a UAV-assisted surveillance system, UAVs are equipped with learning-based object recognition models and can collect surveillance images. Keywords: drones; UAV; unmanned autonomous vehicles; unmanned ground vehicles EN drones UAV unmanned autonomous vehicles unmanned ground vehicles 731 734 4 10/31/23 20231001 NES 231001 Recently, research on autonomous mobility control has been actively and widely conducted for various applications. [Extracted from the article]

In this study, we introduce a loosely coupled relative position estimation method that utilizes a decentralized ultrawideband (UWB), Global Navigation Support System and inertial navigation system for flight controllers (FCs). Key obstacles to multidrone collaboration include relative position errors and the absence of communication devices. To address this, we provide an extended Kalman filter‐based algorithm and module that correct distance errors by fusing UWB data acquired through random communications. Via simulations, we confirm the feasibility of the algorithm and verify its distance error correction performance according to the amount of communications. Real‐world tests confirm the algorithm's effectiveness on FCs and the potential for multidrone collaboration in real environments. This method can be used to correct relative multidrone positions during collaborative transportation and simultaneous localization and mapping applications. [ABSTRACT FROM AUTHOR]

This study investigates the problem of inertial navigation system's (INS's) vertical channel instability, which primarily results from the positive gravity feedback that takes place in the numerical integration step of the accelerometer measurements. Traditional solutions to the problem include using information from auxiliary sensors, typically barometers and global navigation satellite systems (GNSSs), integrated into INSs through feedback control loops (referred to as mechanizations) and/or Kalman filters (KFs). Most studies on this subject lack clarity regarding the methodology used for the mechanization tuning (by showing excessive empiricism) and/or disregard the benefits of using optimal control techniques for the purpose of the latter. This study, therefore, reviews the main mechanization used for vertical channel stabilization of baro‐inertial integrations and presents, as its main contribution, the performance analysis of new tuning techniques based on optimal control theory, namely, by linear quadratic regulator (LQR) and by minimization of performance indices such as the integral absolute error (IAE), integral squared error (ISE), integral of time multiplied absolute error (ITAE), and integral of time multiplied squared error (ITSE). To validate the proposed tunings, the integrated altitude and vertical velocity mean errors (MEs) and standard deviations (SDs) are used as metrics, which are compared with the corresponding MEs/SDs of the empirical tuning with highest performance/acceptance in the literature. We also evaluate the ability of the mechanizations to fulfill their ultimate goal, namely, to track the reference barometer input, by also attenuating its noise. Based on simulated and experimental results, the present study demonstrates and validates the effectiveness/robustness of the proposed optimal tunings, particularly those based on LQR and ITAE minimization. [ABSTRACT FROM AUTHOR]

Aiming to reduce the impact that the measurement uncertainty of nonlinear system brings to filtering accuracy, a novel sparse grid quadrature filter (SGQF) algorithm based on the double‐state chi‐square test is proposed. And the strap‐down inertial navigation system (SINS)/global navigation satellite system (GNSS) integrated navigation system is used to verify the algorithm. A discrete‐time system model with uncertain measurement is constructed, then the system state through the SGQF algorithm is estimated. To determine the uncertainty of the measurement, the double‐state chi‐square test is introduced. By comparing the results of the state recursor, the chi‐square test value is obtained and it is used to update the measurement noise. Further, the system state and error covariance are re‐estimated. So far a robust sparse grid filter algorithm with statistical adjustment of the measurement noise is obtained. The proposed algorithm can overcome the influence of error statistical characteristic amplification on system filtering accuracy. Taking the SINS/GNSS integrated navigation system as the application object, off‐line simulation experiments and online flight tests verify the robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We propose a navigation system combining sensor‐aided inertial navigation and prior‐map‐based localization to improve the stability and accuracy of robot localization in structure‐rich environments. Specifically, we adopt point, line, and plane features in the navigation system to enhance the feature richness in low‐texture environments and improve the localization reliability. We additionally integrate structure prior information of the environments to constrain the localization drifts and improve the accuracy. The prior information is called structure priors and parameterized as low‐dimensional relative distances/angles between different geometric primitives. The localization is formulated as a graph‐based optimization problem that contains sliding‐window‐based variables and factors, including Inertial Measurement Unit, heterogeneous features, and structure priors. A limited number of structure priors are selected based on the information gain to alleviate the computation burden. Finally, the proposed framework is extensively tested on synthetic data, public data sets, and, more importantly, on the real Unmanned Aerial Vehicle flight data obtained from both indoor and outdoor inspection tasks. The results show that the proposed scheme can effectively improve the accuracy and robustness of localization for autonomous robots in civilian applications. [ABSTRACT FROM AUTHOR]

Summary: This article investigates the state estimation problem of the nonlinear system with the large process prior uncertainty but high‐accuracy measurement information, the situation is prone to occur in the inertial navigation system (INS)/global navigation satellite system (GNSS) tightly coupled navigation system. Furthermore, the unknown heavy‐tailed measurement noises induced by the inaccurate prior navigation information and random measurement outliers are also considered. Given existing methods such as progressive cubature Kalman filter (PCKF) cannot effectively solve the above issues, a novel robust PCKF with variable step size (RVSS‐PCKF) is proposed. First, a new Gaussian‐uniform‐mixing inverse Gamma (GUMIG) distribution is presented to model the variable step size and measurement noise. Next, the GUMIG distribution is expressed as a hierarchical Gaussian presentation and then RVSS‐PCKF is derived with the help of the variational Bayesian (VB) inference. In the filter, the state, variable step size and noise statistic are jointly estimated by the fixed‐point iterations. Finally, the simulations and real data of the tightly coupled navigation illustrate the superiority of the filter in terms of accuracy and steady‐state performance. [ABSTRACT FROM AUTHOR]

We developed an environmentally adaptive under‐ice navigation framework that was deployed in the Arctic Beaufort Sea during the United States Navy Ice Exercise in March 2020 (ICEX20). This navigation framework contained two subsystems developed from the ground up: (1) an on‐board hydrodynamic model‐aided navigation (HydroMAN) engine, and (2) an environmentally and acoustically adaptive integrated communication and navigation network (ICNN) that provided acoustic navigation aiding to the former. The HydroMAN synthesized measurements from an inertial navigation system (INS), ice‐tracking Doppler velocity log (DVL), ICNN and pressure sensor into its self‐calibrating vehicle flight dynamic model to compute the navigation solution. The ICNN system, which consisted of four ice buoys outfitted with acoustic modems, trilaterated the vehicle position using the one‐way‐travel‐times (OWTT) of acoustic datagrams transmitted by the autonomous underwater vehicle (AUV) and received by the ice buoy network. The ICNN digested salinity and temperature information to provide model‐assisted real‐time OWTT range conversion to deliver accurate acoustic navigation updates to the HydroMAN. To decouple the contributions from the HydroMAN and ICNN subsystems towards a stable navigation solution, this article evaluates them separately: (1) HydroMAN was compared against DVL bottom‐track aided INS during pre‐ICEX20 engineering trials where both systems provided similar accuracy; (2) ICNN was evaluated by conducting a static experiment in the Arctic where the ICNN navigation updates were compared against GPS with ICNN error within low tens of meters. The joint HydroMAN‐ICNN framework was tested during ICEX20, which provided a nondiverging high‐resolution navigation solution—with the majority of error below 15 m—that facilitated a successful AUV recovery through a small ice hole after an 11 km untethered run in the upper and mid‐water column. [ABSTRACT FROM AUTHOR]

In railway transportation, track geometry irregularity is one of the main factors in controlling train safety. At present, railway practitioners typically use the track geometry car (TGC) based on the inertial navigation system to inspect track irregularities. However, TGCs are quite expensive, and their inspection interval is relatively long. Among a variety of emerging methods, using vehicle responses to estimate track irregularities seems very promising as it enables a cheaper and more efficient solution. In this work, an extended auto‐encoder (EAE) is proposed to estimate the track longitudinal irregularity through car body acceleration. The mean absolute percentage errors of the estimated results on the simulated and the real‐world dataset are 2.67% and 3.75%, respectively, which is 50%–55% lower than the traditional neural network. In the frequency domain, the characteristic wavelengths of 5.4 and 32 m can be effectively identified. Besides, the Bayesian deep learning (BDL) method is introduced to improve the EAE and estimate the confidence interval of the track longitudinal amplitude. A metric (coverage width and error) for evaluating and optimizing the performance of the estimated interval is proposed. The interval estimation result in the time domain has a 98% correct coverage rate of the ground truth and 93% in the frequency domain. Within the error range of plus/minus one standard deviation, the EAE model has an estimation accuracy of 94.2% for the standard deviation of track longitudinal irregularity, and the BDL‐EAE can even reach 100%. Compared with the existing methods, our proposed model only requires car body acceleration and has the potential to use ordinary in‐service trains for onboard track inspection. [ABSTRACT FROM AUTHOR]

Efficient multi‐sensor fusion is crucial to provide accurate pose estimates for navigating various next‐generation autonomous vehicles such as self‐driving cars, personal air vehicles, urban air mobilities, and electronic vertical take‐off and landing aircraft with respect to a unified global reference frame. The integration of the Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) provides globally uniform coordinate information, and the Visual‐Inertial Odometry (VIO) gives stable and accurate local pose estimates. However, the error of the integrated INS/GNSS increases rapidly in GNSS‐challenged environments, and the VIO system suffers from the long‐term error accumulation. To cope with these difficulties, this paper proposes a new tightly coupled Visual‐Inertial‐GNSS (VIG) system to achieve robust and drift‐free pose estimation utilising GNSS raw measurements, image features, and inertial measurements. A novel compressed state constraint Kalman Filter (CSCKF) is formulated to combine time‐propagated feature measurements and differential GNSS observations to aid INS. Compared to the conventional integrated navigation systems, the proposed CSCKF‐based VIG system is advantageous in maintaining the minimum number of states without unnecessary state augmentation and no loss of performance. The proposed system is evaluated by field experiments in different GNSS availability situations. The results show that accuracy is improved significantly in GNSS‐degraded environments compared to that of the conventional systems. [ABSTRACT FROM AUTHOR]

This paper proposes a three‐dimensional optimal terminal velocity control guidance for multicopter intercepting maneuvering drone with equal maneuverability level. We investigate a novel multi‐rate extended Kalman filter‐based approach to estimate target's velocity and acceleration by fusing the measurements of onboard machine vision algorithm, electro‐optical pod, and inertial navigation system. The estimated target information is helpfully incorporated into the formulation of relative kinematics, based on which the guidance problem is naturally decoupled and optimal control theory can be directly leveraged to obtain the analytical solution. The key aspect of the proposed law lies in its low maneuverability requirement due to the inclusion of target information. The working principle and characteristics of the guidance command are theoretically analyzed to support our findings. Both numerical simulations and flight experiments are performed to validate the proposed approach and demonstrate its advantages. [ABSTRACT FROM AUTHOR]

Summary: In many applications (i.e., navigation of vehicles in traffic, museum guide, guidance to firefighters in poor visibility, and elderly care assistance systems), location‐based services (LBS) rely on the global positioning system (GPS) for accurate positioning. LBS can be challenging to employ in an indoor environment because of the lack of a line‐of‐sight (LOS) component and other signal propagation issues. Over the last decade, researchers worldwide have been motivated to solve the problem of continuous indoor positioning in situations when GPS is ineffective. We thoroughly reviewed research conducted by many scholars and compiled this survey article, which provides a comprehensive overview of indoor pedestrian navigation systems, prevalent step length estimation models, and the current state of the art. There are two key strategies in which substantial work on indoor localization is carried out. One is a dead reckoning in which the present location of the pedestrian is calculated based on details about previous location, and the other is an inertial navigation system in which the forward acceleration from an inertial sensor strapped down to the user's body is integrated twice to establish the current location. The step length is a crucial component of the inertial sensor‐based navigation in both these strategies. For different users, step length is variable and also changes at different walking speeds for each user. As a result, the focus of this survey is on step length estimation models. The survey describes and compares various approaches to estimating step length from various perspectives, including the research method used, the length of the test path, various walking speeds, the location of the sensor device on the user's body, and the accuracy achieved in estimating step length. Because inertial sensors are embedded in smartphones, it is vital to identify the smartphone mode (i.e., phoning, texting, or pocket) during each step taken while using smartphone‐based indoor navigation. The report concludes with a quantitative evaluation of the performance of smartphone mode recognition modules. At the end, we list out challenges still to be addressed for scholars working on indoor navigation. [ABSTRACT FROM AUTHOR]

The issue of non-linear robust state estimation in the integration of a strapdown inertial navigation system and global positioning system is addressed in this study. Based on the cubature Kalman filtering frame, a non-linear robust filter called a robust cubature Kalman filter (RCKF) was introduced to address the outliers and the inaccurate model. It has been found that the determination of an optimal restriction parameter is crucial for maintaining the robustness and accuracy of the non-linear robust filter. Unfortunately, the value of this restriction parameter is always determined by experience. In this study, an iterative strategy was proposed to adaptively attain the optimal restriction parameter without much previous experience. To improve the computational stability of the iterative non-linear robust filter, a singular value decomposition strategy was adopted simultaneously. Two case studies indicate that the iterative RCKF can achieve greater robustness and accuracy using the methodology discussed in this study. [ABSTRACT FROM AUTHOR]

The measurement noise covariance R plays a vital role in the Kalman filter (KF) algorithm. Traditionally, a constant R is obtained by experience or experiments. However, the KF cannot achieve optimal estimation using constant R under non-Gaussian conditions. A robust strategy for adaptive estimation of R is proposed to suppress the influence of non-Gaussian noise on the in-motion alignment performance of the Doppler velocity log (DVL) velocity-aided strapdown inertial navigation system (SINS). Furthermore, an improved Sage-Husa robust adaptive KF algorithm (SHRAKF) based on the Mahalanobis distance (MD) algorithm is proposed to handle the outliers that frequently occur within the complicated underwater environment. The contributions of this work are twofold--the SHRAKF (1) designs a robust strategy to adaptively estimate R in real time and (2) further improves filtering robustness and adaptability with the MD algorithm, conditional on the DVL outputs being contaminated by non-Gaussian noise. A semi-physical simulation experiment for SINS/DVL in-motion alignment based on the test data is carried out, and the experimental results show that the SHRAKF adaptively estimates R in real time and effectively suppresses observational outliers. For non-Gaussian noise pollution, including outliers and heavy-tailed noise, the SHRAKF performs better than traditional methods. [ABSTRACT FROM AUTHOR]

The existing terrain aided navigation (TAN) has the disadvantage that the navigation performance is limited by the size of matching area and terrain features. Therefore, this study proposes a method of optimal selection and adaptability analysis of matching area for TAN. According to the heading angle of the carrier, inertial navigation system positioning error and performance index, the optimal matching area is selected in real time. First, the matching area selection and adaptability analysis are carried out, and the influence of the matching area on the positioning performance is analysed by using the Beijing Internal TAN II algorithm. Then, the model parameters of the optimal matching area are determined through theoretical calculation and simulation. Finally, it is compared with the typical matching area selecting method. The test results show that the optimal selection and adaptability analysis method can improve the computational efficiency of TAN system, and improve the positioning accuracy of TAN system. [ABSTRACT FROM AUTHOR]

In highly dynamic scenes, beamforming can alleviate satellite signal attenuation and distortion caused by null widening. In this article, a beam steering scheme based on strapdown inertial navigation system (SINS) aid is proposed. Firstly, the Hermite interpolation algorithm is used to calculate satellite position coordinates to reduce the amount of calculation. On the other hand, the satellite position coordinates are converted to the antenna coordinate system, and the beam direction is calibrated based on the attitude angle measured by the SINS to ensure the optimal beam gain. Finally, the proposed scheme is verified through a simulation, and the influence of SINS measurement errors and interpolation errors on the beamforming performance is evaluated. The simulation results show that the proposed method can effectively improve and stabilize the signal‐to‐noise ratio and reduce the influence of anti‐interference processing on satellite signals close to the interference direction in dynamic scenes. [ABSTRACT FROM AUTHOR]

Aiming to improve the position and velocity precision of the INS/GNSS system during GNSS outages, a novel system that combines unscented Kalman filter (UKF) and nonlinear autoregressive neural networks with external inputs (NARX) is proposed. The NARX‐based module is utilized to predict the measurement updates of UKF during GNSS outages. A new offline approach for selecting the optimal inputs of NARX networks is suggested and tested. This approach is based on mutual information (MI) theory for identifying the inputs that influence each of the outputs (the measurement updates of UKF) and lag‐space estimation (LSE) for investigating the dependency of these outputs on the past values of the inputs and the outputs. The performance of the proposed system is verified experimentally using a real dataset. The comparison results indicate that the NARX‐aided UKF outperforms other methods that use different input configurations for neural networks. [ABSTRACT FROM AUTHOR]

The Mars 2020 rover, Perseverance, landed in Jezero crater (18.4663°N, 77.4298°E) on February 18, 2021 to collect samples from Mars that could be returned to Earth by a future Mars Sample Return campaign. While providing a rich sampling opportunity, Jezero also contains numerous landing hazards including scarps, canyons, mesas, dune fields, rock fields, and smaller craters. The Mars 2020 onboard inertial navigation system, which is the same as that used by the Mars Science Laboratory (MSL), only provides a very coarse inertially propagated position, which can have error as large as 3.2 km. The Lander Vision System (LVS) was added to Mars 2020 to reduce this position knowledge error to less than 40 m with respect to an on‐board reference map of the landing area. LVS uses a reference map on board to compare with the descent images for lander localization during the terminal stage of Entry, Descent and Landing (EDL). Because the reference map is used directly during EDL, it is critical that it have as little spatial and photometric error as possible. Photometrically, it should resemble as much as possible the real descent images to allow reliable terrain matching. Spatially, it should match as faithfully as possible the real, underlying terrain and contribute minimal error to the final localization solution. On February 18, 2021, the LVS reference map passed its ultimate test. The LVS system executed Terrain Relative Navigation (TRN) flawlessly based on the LVS reference map. In addition to its use for TRN, the local hazard map is also registered to the LVS reference map so that the safe target selection (STS) system can select a safe and reachable landing site. The final error between the site targeted by the STS system and the real landing site is estimated at only 5 m. The exceptional performance of LVS indicates that the reference map met mission requirements with comfortable margin. LVS on Mars 2020 represents the first ever use of a reference map during spacecraft EDL. This breakthrough will have profound implications for future lander missions and the scope of scientific inquiry they are able to address. In this paper, we will describe the necessary precursor steps to building the Jezero Crater LVS map, including the methodology to dejitter Context Imager (CTX) images, improve the CTX sensor model, as well as the process used to validate the LVS map accuracy. Key Points: The Lander Vision System used for precision landing by the Mars 2020 mission required unprecedented fidelity in the onboard reference mapThis study describes methods used to generate an onboard reference map with the required fidelityThis study describes methods used to validate the onboard reference map [ABSTRACT FROM AUTHOR]

Factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS‐INS integration. This study evaluates both loosely and tightly coupled integrations of GNSS code pseudorange and INS measurements for real‐time positioning, using both conventional EKF and FGO with a dataset collected in an urban canyon in Hong Kong. The FGO strength is analyzed by degenerating the FGO‐based estimator into an "EKF‐like estimator." In addition, the effects of window size on FGO performance are evaluated by considering both the GNSS pseudorange error models and environmental conditions. We conclude that the conventional FGO outperforms the EKF because of the following two factors: (1) FGO uses multiple iterations during the estimation to achieve a robust estimation; and (2) FGO better explores the time correlation between the measurements and states, based on a batch of historical data, when the measurements do not follow the Gaussian noise assumption. [ABSTRACT FROM AUTHOR]

Navigation systems of safety‐critical unmanned aircraft need an alternative position aiding source to global navigation satellite system. One promising alternative, is position measurements from phased‐array radio systems. The presented navigation system consist of a multiplicative extended Kalman filter that utilize these measurements, along with an exogenous altitude measurement, to aid an inertial navigation system. The reliability and accuracy of the navigation system is demonstrated through beyond‐visual‐line‐of‐sight flights with a fixed‐wing unmanned aerial vehicle, without the use of global navigation satellite systems. This uses a conventional guidance controller module, which also monitor the navigation quality. [ABSTRACT FROM AUTHOR]

Optical and acoustic stereo imaging has great potential for the precise and consistent localization of intervention underwater robots; however, it is still being explored due to its sensing limitations and various technical challenges. This study presents a novel localization method by combining an inertial navigation system and an optical and acoustic stereo imaging system. As a strategy for localization correction relative to underwater structures, the robot's pose is estimated based on a single acoustic image using a sonar simulator for mid‐range localization, and a robust visual tracking using a 3‐D wireframe model is employed for high‐precision localization near the target structures. The performance of the proposed technique was demonstrated through experimental validation using real data obtained from a test tank. [ABSTRACT FROM AUTHOR]

The future of deep space exploration depends upon technological advancement towards improving spacecraft's autonomy and versatility. This study aims to examine the feasibility of autonomous orbit determination using advanced accelerometer measurements. The objective of this research is to ascertain specific sensor requirements to meet pre‐defined mission navigation error budgets. Traditional inertial navigation (dead reckoning and external aiding) is not considered. Instead, measurements from pairs of advanced, highly sensitive accelerometers (e.g., cold atom accelerometers) are used onboard to determine gravity field gradients, which are then correlated to onboard gravity maps and used to determine orbital information. Linear Covariance Theory helps to efficiently conduct an error budget analysis of the system. This error budget analysis helps to determine the effect of specific error sources in the sensor measurements, thereby providing information to rank and compare relevant sensor parameters and determine an optimal sensor configuration for a given space mission. The procedure is repeated to evaluate different accelerometer configurations and sensor parameters. [ABSTRACT FROM AUTHOR]

The in‐motion alignment for the underwater strapdown inertial navigation system is still a challenging problem due to various disturbances in the underwater environment. In this paper, a novel in‐motion alignment method, based on the Lie group representation, is developed. In this method, the process model is rewritten using the Lie group of the constant attitude matrix between two inertial frames as the state. An exact linear measurement model is constructed by analyzing the effect of the sensor errors in calculating the velocity vector. Next, the state‐dependent Lie group filter is designed basing on accurate derivation expressions for the covariance matrices of state‐dependent noises. The simulation and experiment results demonstrate that the proposed method can achieve better alignment accuracy and time than the existing method. The accuracy improves by 70% with the quaternion Kalman filter. [ABSTRACT FROM AUTHOR]

Modern navigation systems integrate the global positioning system (GPS) with an inertial navigation system (INS), which complement each other for correct attitude and velocity determination. The core of the INS integrates accelerometers and gyroscopes used to measure forces and angular rate in the vehicular inertial reference frame. With the help of gyroscopes and by integrating the acceleration to compute velocity and distance, precision and compact accelerometers with sufficient accuracy can provide small‐error location determination. Solid‐state implementations, through coherent readout, can provide a platform for high performance acceleration detection. In contrast to prior accelerometers using piezoelectric or capacitive readout techniques, optical readout provides narrow‐linewidth high‐sensitivity laser detection along with low‐noise resonant optomechanical transduction near the thermodynamical limits. Here an optomechanical inertial sensor with an 8.2 µg Hz−1/2 velocity random walk (VRW) at an acquisition rate of 100 Hz and 50.9 µg bias instability is demonstrated, suitable for applications, such as, inertial navigation, inclination sensing, platform stabilization, and/or wearable device motion detection. Driven into optomechanical sustained‐oscillation, the slot photonic crystal cavity provides radio‐frequency readout of the optically‐driven transduction with an enhanced 625 µg Hz−1 sensitivity. Measuring the optomechanically‐stiffened oscillation shift, instead of the optical transmission shift, provides a 220× VRW enhancement over pre‐oscillation mode detection. [ABSTRACT FROM AUTHOR]

Instances of spoofing and jamming of global navigation satellite systems (GNSSs) have emphasized the need for alternative navigation methods. Aerial navigation by magnetic map matching has been demonstrated as a viable GNSS‐alternative navigation technique. Flight test demonstrations have achieved accuracies of tens of meters over hour‐long flights, but these flights required accurate magnetic maps which are not always available. Magnetic map availability and resolution vary widely around the globe. Removing the dependency on prior survey maps extends the benefits of aerial magnetic navigation methods to small unmanned aerial systems (sUAS) at lower altitudes where magnetic maps are especially undersampled or unavailable. In this paper, a simultaneous localization and mapping (SLAM) algorithm known as FastSLAM was modified to use scalar magnetic measurements to constrain a drifting inertial navigation system (INS). The algorithm was then demonstrated on real magnetic navigation flight test data. Similar in performance to the map‐based approach, MagSLAM achieved tens of meters accuracy in a 100‐minute flight without the use of a prior magnetic map. Aerial SLAM using Earth's magnetic anomaly field provides a GNSS‐alternative navigation method that is globally persistent, impervious to jamming or spoofing, stealthy, and locally accurate to tens of meters without the need for a magnetic map. [ABSTRACT FROM AUTHOR]

Angular acceleration sensors technologies are evolving and emerging in various applications. In this paper, we propose an angular accelerometer‐based inertial navigation system. One application of such systems can be highly maneuvering platforms where direct measurement of angular acceleration is desired. To that end, a review of modern angular acceleration sensor technologies and their possible applications is provided. Two angular acceleration‐based inertial navigation system architectures are suggested, and their equations of motion are derived. Additionally, an analytical assessment for short time periods of pure inertial navigation is provided with a comparison to traditional and gyro‐free inertial navigation architectures. [ABSTRACT FROM AUTHOR]

Pemphigus vulgaris is a rare chronic blistering skin disease resulting from IgG autoantibodies directed against transmembrane desmosomal glycoprotein desmoglein 3 and is the most common form of pemphigus. Since interleukin‐1 receptor‐associated kinase (IRAK‐1)/nuclear factor‐kappa B (NF‐kappa B) pathway plays an essential role in the pathogenesis of autoimmune diseases, the aim of the present study was to explore the role of polymorphisms in three genes, named IRAK1 (rs3027898), NFKBIA (rs696) and NFKB1 (‐94ATTG insertion/deletion variant, ‐ rs28362491), in PV susceptibility. Forty‐four unrelated patients with PV (23 males) were enrolled in the study. Additionally, 77 ethnic matching healthy volunteers (45 males) with no personal or family history of chronic autoimmune or infectious diseases were studied. Strong statistical significant difference was observed between PV patients and controls for polymorphism ‐94 insertion/deletion ATTG in the promoter region of NFKB1 gene (P = 0.00005). Additional dedicated studies in larger groups of patients of various ethnicities are needed to replicate and confirm the preliminary findings. [ABSTRACT FROM AUTHOR]

Currently, studies on autonomous driving are being actively conducted. Vehicle positioning techniques are very important in the autonomous driving area. Currently, the global positioning system (GPS) is the most widely used technology for vehicle positioning. Although technologies such as the inertial navigation system and vision are used in combination with GPS to enhance precision, there is a limitation in measuring the lane and position in shaded areas of GPS, like tunnels. To solve such problems, this paper presents the use of LED lighting for position estimation in GPS shadow areas. This paper presents simulations in the environment of three‐lane tunnels with LEDs of different color temperatures, and the results show that position estimation is possible by the analyzing chromaticity of LED lights. To improve the precision of positioning, a fuzzy logic system is added to the location function in the literature [1]. The experimental results showed that the average error was 0.0619 cm, and verify that the performance of developed position estimation system is viable compared with previous works. [ABSTRACT FROM AUTHOR]

The stationary self‐alignment and calibration (SSAC) for a low‐cost MEMS IMU is quite challenging due to the poor observability of an inertial system under static condition and the significant sensor errors of MEMS inertial sensors. This research proposes to employ IMU rotations to improve the system observability and estimability regarding the SSAC of a low‐cost MEMS IMU. IMU rotations about the X, Y, and Z axes are employed in this paper. The analytic estimation algorithm for each error state is derived and the observability of the system with IMU rotation is analyzed. As the observability analysis will not provide clues about how well an error state can be estimated, the estimability analysis is also conducted based on the eigenvalues and eigenvectors from the covariance matrix in the Kalman filter. Tests are conducted with a tri‐axial turntable to verify the improvements on system observability and estimability brought by IMU rotations. Of both theoretical analysis and results indicated with proper IMU rotations, only azimuth error still remains unobservable, and the IMU rotation also significantly improves the estimability of all error states, including the unobservable azimuth. [ABSTRACT FROM AUTHOR]

The Kalman Filter (KF) is the most popular approach in sensor fusion and navigation applications. Improving the accuracy of estimation may increase the navigation accuracy. KF is the best choice for minimum variance optimal estimation, but it is not the best approach to improve some of the main and classic features such as steady‐state error reduction. These features are more important than optimality for applications like accuracy improvement in navigation. Also, these features and their solutions have been addressed in classic control techniques as a widely known subject. Therefore, some similar improvements in the estimation problem are discussed in the literature. These studies try to conserve the optimality in the covariance of the error, but classic and optimal features cannot be achieved simultaneously. Hence, these methods are not as efficient as expected. This paper intends to improve the estimation of position and velocity in navigation problems by integrating classic and modern control techniques. Beside the classic features and advantages, it may be seen that the resulted covariance of error is not minimum anymore, but it is comparable with the optimal methods. [ABSTRACT FROM AUTHOR]

Combining collocated high‐rate Global Navigation Satellite Systems (GNSS) and accelerometers produces broadband seismogeodetic displacements. However, accelerometer data must be heavily downweighted due to their baseline errors originating primarily in instrument rotations, and therefore their contribution to seismogeodetic displacements is seriously underestimated. We further introduced a gyroscope into this classic seismogeodesy to mitigate baseline errors and formulated advanced six‐degree‐of‐freedom (6‐DOF) seismogeodesy without undervaluing accelerometer data. A shake table holding one GNSS antenna, four accelerometers, and one gyroscope was used to simulate waveforms from the 2010 Mw 7.2 El Mayor‐Cucapah earthquake. We found that the displacements derived from the 6‐DOF seismogeodesy were up to 68% more accurate than those from the classic seismogeodesy over 0.04–0.4 Hz. Moreover, broadband rotations containing the permanent components were also generated, which were unachievable by integrating gyroscope data. We believe that the 6‐DOF seismogeodesy is capable of improving both source rupture studies of large earthquakes and high‐rise monitoring under strong seismic waves. Plain Language Summary: The faulting mechanism of large earthquakes has always been an enigma to the seismology community. One important reason is that we cannot measure accurately the low‐frequency (e.g., <0.1 Hz) displacements at near‐source stations using existing techniques. In particular, seismometer recovered low‐frequency displacements are normally biased and incomplete due to intrinsic instrument limitations; Global Navigation Satellite Systems is able to obtain unbiased broadband (e.g., 0–5 Hz) displacements, but they are over 2 orders of magnitude noisier than seismometer measurements. We therefore develop an advanced technique that combines collocated Global Navigation Satellite System receivers, accelerometers, and gyroscopes to capture highly accurate earthquake‐induced displacements and also rotations, spanning the frequencies from 0 to tens of hertz. We demonstrate that the accuracy of low‐frequency displacements can be improved by up to 68% in contrast to traditional measuring techniques, and the permanent rotations can also be recovered, which can never be achieved by only inertial sensors (i.e., gyroscopes and accelerometers). We believe that this new technique can assist in deepening our understanding of the rupture process of large earthquakes, and the broadband rotations have the potential to unveil more unknowns about earthquake sources and Earth media beyond our present knowledge. We can get more prepared for future destructive earthquakes. Key Points: Six‐degree‐of‐freedom (6‐DOF) seismogeodesy improves displacement accuracy by up to 68% compared to high‐rate GNSS and accelerometersBroadband coseismic rotations containing permanent components are producedSource studies of large earthquakes and health monitoring of engineering structures will benefit from 6‐DOF seismogeodesy [ABSTRACT FROM AUTHOR]

Kinematic constrained navigation is a subset of model‐aided navigation systems that is attractive because of its independence from extra hardware equipment. In this study, the error‐based Kalman Filter is used for the data fusion process through feedback strategy, and kinematic constraints are utilized to improve Inertial Navigation System‐Doppler Velocity Log (INS‐DVL) navigation performance. Here, the pseudo‐measurement method is used for applying kinematic constraints. Real offline data obtained from field tests of an instrumented vessel in shallow water demonstrate that applying kinematic constraints improves navigation performance and robustness against initial condition uncertainty. Repeatability of results is also investigated for three different maneuvers. [ABSTRACT FROM AUTHOR]

This paper presents a smart hybrid vision‐aided inertial navigation system (VAINS) for enhancing the navigation accuracy of UAVs in GNSS‐denied environments. The proposed system consists of GNSS, INS, monocular visual odometry (VO) based on optical flow and regression trees, a Gaussian process regression (GPR), and extended Kalman filter (EKF) for data fusion. Although a variety of monocular VO‐based on photogrammetric, structure from motion, and machine learning approaches have been proposed to assist the navigation process, the accuracy of 3‐D positioning using these techniques is still affected by some factors such as the lack of the observed features, incorrect matches, and the accumulated positioning drift errors. Therefore, the development of a reliable navigation system that can handle such challenges is necessary. In this system, the GPR algorithm is deployed to model these errors when the GNSS signal is available and to correct them when the GNSS signal is lost. [ABSTRACT FROM AUTHOR]

In the context of pedestrian navigation and Augmented Reality applications in urban environments, we propose to fuse the pose estimated through a vision process, thanks to a precisely known 3D model, with inertial and magnetic measurements. First, this allows for updating a Pedestrian Dead‐Reckoning process and improving the positioning accuracy. Second, a trusted pose estimate allows us to reproject 3D Geographical Information System content in Augmented Reality with qualified confidence. Because 3D Geographical Information System data are provided by many sources inducing an inhomogeneous precision and level of quality, being able to qualify these 3D contents is important to validate their relevance to use them. A long pedestrian path of 3 km in an urban environment with a sparsely known 3D model of urban furniture was conducted. This has permitted validation of the contribution of sensor fusion that improves the positioning accuracy and allows characterization of the 3D Geographical Information System content directly on‐site using Augmented Reality. Performance is presented in terms of positioning accuracy in urban spaces. © 2018 Institute of Navigation. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to enhance unmanned aerial vehicle (UAV) autonomous navigation, without adding extra load to the vehicle. The proposed approach employs the UAV vehicle dynamic model to aid the navigation estimation in a Global Navigation Satellite Systems (GNSS)‐denied environment, without the need to model any part of the UAV, and avoids the requirement for special equipment during the modeling procedures typically required for vehicle dynamic model‐aided navigation. Taking advantage of the available information from previous flights during availability of GNSS, and with the aid of a hybrid machine learning approach, the proposed technique is able to enhance the navigation accuracy during GNSS outage, despite the massive drift occurring from utilizing a low‐cost inertial measurement unit (IMU) during the outage period. Different scenarios are investigated to prove the robustness of the proposed technique, and the results are compared to two types of IMUs with the aid of an inverse mechanization IMU simulator. © 2018 Institute of Navigation [ABSTRACT FROM AUTHOR]

In conventional navigation systems, inertial sensors consist of accelerometers and gyroscopes. These sensors suffer from in-built errors, accumulated drift and high-level noise sensitivity. The accurate gyroscopes are expensive and not suitable in cost-effective applications. To minimise such disadvantages, one solution is the combination of inertial sensors with different aiding sensors. To lower the cost, utilisation of redundant accelerometer structure as gyro-free inertial measurement unit (GFIMU) has been proposed. In this study, Gyro-Free navigation errors using four tri-axial accelerometers are illustrated. Compensation of errors in terms of angular velocity and position estimation is verified based on adding a simple gyroscope, inexpensive stereo cameras as well as creating an easy to use topological map. The topological map is easily created by means of scale-invariant feature transform method. The estimation of angular velocity is corrected on the basis of fusing the measurements from GFIMU and a simple gyroscope using unscented Kalman filter. The correction of position is performed by comparing the estimated position from GFIMU and observation of stereo cameras together with topological map. The results of the research show that the collaboration of GFIMU, stereo cameras and simple gyroscope will improve the robustness and accuracy of navigation, significantly. [ABSTRACT FROM AUTHOR]

The initial alignment is vital to the inertial navigation system of a cruise vehicle. The existing initial fine alignment methods in the erection process are mostly dependent on other equipment to provide measurements, which increase the complexity and cost of the system and may cause some measurement error and noise. To solve this problem, an initial fine alignment method based on self-contained measurement is proposed and executed during the erection process with a static base. The self-contained measurement is obtained through the modelling of a multi-level dynamic lever arm. Using the self-contained measurement, the fine alignment model is derived and the precision of initial fine alignment can be improved. Finally, experiments were carried out and verified the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Underwater navigation of autonomous underwater vehicles (AUVs) is a challenging task that requires the fusion of multiple sensors used as aiding to the vehicle inertial navigation system. In this paper, we focus on the problem of fusing heading measurements under different maneuvering conditions. We analyze the observability of the heading measurement fusion problem and derive the observable and unobservable subspaces as a function of the AUV's maneuver. Using this analysis, we are able to predict the convergence of the error states for different maneuvers and thus identify the quality of the navigation solution at different mobility patterns. As expected, the results show that the down misalignment error and gyro z‐axis bias are always observable. In addition, our results showed that for some maneuvers, the rest of the gyro biases also become observable. Our numerical simulation and sea experiments verify our analytic solutions. Copyright © 2018 Institute of Navigation. [ABSTRACT FROM AUTHOR]

Even though technology-aided personal navigation is an extensively studied research topic, approaches based on inertial sensors remain challenging. In this study, the authors present a comparison between different inertial systems, investigating the impacts of on-body placement of Inertial Measurement Units (IMUs) and, consequently, of different algorithms for the estimation of the travelled path on the navigation accuracy. In particular, the system performance is investigated considering two IMU placements: (i) on the feet and (ii) on the lower back. Sensor fusion is then considered in order to take advantage of the strengths of each placement. The results are validated through an extensive data collection in indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

Limb length is a useful parameter in the assessment of common musculoskeletal disorders such as limb length discrepancy. The measurement variation among rates adversely affects the quantitative aspect of assessments and introduces a greater subjectivity in the course of treatment. Common practise for measuring limb length is based on radiographic imaging techniques which are inconvenient, costly and require clinical knowledge. Direct instruments are difficult to use with patients due to susceptibility to human error in determining the position of the rotational joint. In this study, the determination of limb length is automated using a contemporary algorithm which applies curvature to the measurements from a low-cost and miniaturised inertial sensor, primarily used in the bio-kinematic research. The motion artefacts contribute to the ultimate estimations and, in this approach, a least noise threshold model is employed to address the robustness. The proposed estimation technique was validated with real-data observed from 14 healthy subjects comparing with radiographic and direct measurements. The experimental results indicate greater accuracy compared with manual measurements with low root mean squared error percentages with values ranging from 5.34 to 5.84%. Additionally, the mean limb length difference between our estimator and both radiographic measurements and direct measurement was <1.6 cm. [ABSTRACT FROM AUTHOR]

Autonomous micro aerial vehicles (MAVs) have cost and mobility benefits, making them ideal robotic platforms for applications including aerial photography, surveillance, and search and rescue. As the platform scales down, MAVs become more capable of operating in confined environments, but it also introduces significant size and payload constraints. A monocular visual-inertial navigation system (VINS), consisting only of an inertial measurement unit (IMU) and a camera, becomes the most suitable sensor suite in this case, thanks to its light weight and small footprint. In fact, it is the minimum sensor suite allowing autonomous flight with sufficient environmental awareness. In this paper, we show that it is possible to achieve reliable online autonomous navigation using monocular VINS. Our system is built on a customized quadrotor testbed equipped with a fisheye camera, a low-cost IMU, and heterogeneous onboard computing resources. The backbone of our system is a highly accurate optimization-based monocular visual-inertial state estimator with online initialization and self-extrinsic calibration. An onboard GPU-based monocular dense mapping module that conditions on the estimated pose provides wide-angle situational awareness. Finally, an online trajectory planner that operates directly on the incrementally built three-dimensional map guarantees safe navigation through cluttered environments. Extensive experimental results are provided to validate individual system modules as well as the overall performance in both indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of proper integration of an inertial navigation system with an air data system. First, we present a mathematical formalism of the system's mechanization, i.e., navigation equations for spheroidal Earth, air data observation equations, and error propagation equations for optimal Kalman filtering. Next, an analytical observability analysis is conducted and demonstrated by means of a simulation. Finally, we draw conclusions about expected benefits and the most suitable applications. Copyright © 2017 Institute of Navigation [ABSTRACT FROM AUTHOR]

Owing to the ferromagnetism and stray fields of inertial navigation system, component compensation of magnetic field distortion is significant for geomagnetic field vector measurement system. However, traditional scalar compensation methods cannot be used for component compensation of magnetic field distortion, so some improvement works have been researched for component compensation in the study, the major of which is estimating compensation parameters using the Lagrange multiplier method. Experiment results show that the performance of the component compensation method is much better than traditional scalar compensation method. After compensation, the maximal measurement errors of north, vertical, east components and total intensity caused by magnetic field distortion are reduced to 10.4 nT (0.91% of the raw error), 32.5 nT (5.20%), 12.9 nT (1.27%), and 18.4 nT (2.02%), respectively. In addition, compared with previous component compensation method, the proposed method has three advantages: (i) simplified equipment, (ii) easier operation process, and (iii) better generality. [ABSTRACT FROM AUTHOR]

It is proposed to develop a modernized method of aerial navigation using driftmeter measurements. Use of an electro-optical (E/O) system (LANTIRN or Sniper) for ground feature tracking by the pilot, and an independent altitude sensor (baro altimeter or radar altimeter) in conjunction with the INS, is envisioned. The idea is to use measurements taken by the pilot using the E/O system to aid an existing INS, thus automating the navigator's function. The ground feature measurements from the E/O system and the INS output will be used to form measurements provided to a linear Kalman filter (KF) running on the navigation computer to accomplish the INS aiding action. The structured environment (constant speed, constant altitude, and wings level flight while on autopilot) justifies the linearization of the KF kinematic and measurement equations used during an INS aiding session. It is shown that after one short aiding period a 60% reduction in position error covariance is realized. Copyright © 2017 Institute of Navigation. [ABSTRACT FROM AUTHOR]

In-motion alignment of a strapdown inertial navigation system, during slow maneuvers, is studied. Terrestrial velocity is fed back to the navigation system to estimate and compensate for the navigation errors. Observability of the errors is analyzed since the integrated navigation system is not fully observable. Then, the accuracy bounds of the navigation system in different motion scenarios are obtained analytically. Also, in order to minimize the errors of the navigation system, special maneuvers are designed based on the analytical derivations. The analytical results, obtained using the linearized error model, are verified through nonlinear simulation of different maneuvering and non-maneuvering scenarios. It is shown that the proposed analytical method can be used to predict the errors of the navigation system with a high level of accuracy. Copyright © 2017 Institute of Navigation [ABSTRACT FROM AUTHOR]

Marine craft feedback control systems typically require estimates of position, velocity and heading where the wave-induced motions should be suppressed. This paper presents a strapdown inertial navigation system with adaptive wave filtering. Wave filtering based on inertial navigation systems differ from previous vessel-model-based designs that require knowledge of vessel parameters and mathematical models for estimation of thruster and wind forces and moments based on auxiliary sensors. The origin of the inertial navigation system's error states is proven to be uniformly semiglobally exponentially stable. The wave-filtering scheme uses the estimated states of the inertial navigation system to separate the low-frequency motion of the craft from the wave-frequency motions. The observer structure also allows for estimation of the time-varying encounter frequency by using a signal-based frequency tracker or an adaptive observer. Finally, properties following from the triple-redundant sensor packages have been utilized to obtain optimal and robust sensor fusion with respect to sensor performance and faults. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The robustness of the Sandia Inertial Terrain-Aided Navigation (SITAN) algorithm has a pivotal influence on underwater vehicles' Gravity-Aided Inertial Navigation Systems (GAINS). An abrupt glitch of the accuracy of the GAINS will evolve into a disaster when vehicles pass through areas of smooth gravity. In order to resolve vulnerability issues of initial error and linearization error, we propose the Correlation SITAN algorithm with Weight-Reducing Iteration Technique (CSITAN + WRIT), in which the correlation process equals Terrain Contour Matching (TERCOM). The CSITAN algorithm is a Multipoint-based Extended Kalman Filtering (MEKF) method, which can work in real time. First, we need to derive the state equation and observation equation of the Multipoint-based SITAN algorithm based on the principle of the traditional SITAN algorithm. Then the accuracy of the state prediction can be improved, and the linearization error can be reduced by the correlation method based on TERCOM. Finally, the WRIT is utilized to reduce the possible influence of gross errors existing in the results of the MEKF and to extract a value with higher precision. The experimental results show that CSITAN + WRIT can achieve better accuracy and higher success rate of matching and can improve the possibilities of the occurrence of large matching errors than traditional SITAN methods in areas with smooth gravity. Copyright © 2017 Institute of Navigation [ABSTRACT FROM AUTHOR]

The microelectromechanical system ( MEMS) gyroscope provides a new method to design a low-cost untwisting spin platform to be used in a single-axis, stable strap-down inertial navigation system. However, the MEMS gyroscope's drift reduces the effectiveness of the closed-loop feedback control. Thus, a new method of drift suppression is proposed in this paper based on phase space reconstruction in order to improve the platform's performance. The feasibility of the MEMS gyroscope's drift suppression is analyzed using linear decomposition based on phase space. The system drift is estimated by phase space reconstruction. The optimal embedding dimension is found through a grid search. The number of dimensions for dimension reduction analysis is selected according to the minimum eigenvalue. The mapping from the high-dimensional phase space onto the low-dimensional phase space is obtained by minimizing the variance. A Kalman filter is used to compensate the residual sequence further. The proposed method is applied to an untwisting spin platform based on the MEMS gyroscope L3G4200D. The experimental results show that it can reduce the platform drift rate effectively. [ABSTRACT FROM AUTHOR]

This paper describes a vision-based obstacle detection and navigation system for use as part of a robotic solution for the sustainable intensification of broad-acre agriculture. To be cost-effective, the robotics solution must be competitive with current human-driven farm machinery. Significant costs are in high-end localization and obstacle detection sensors. Our system demonstrates a combination of an inexpensive global positioning system and inertial navigation system with vision for localization and a single stereo vision system for obstacle detection. The paper describes the design of the robot, including detailed descriptions of three key parts of the system: novelty-based obstacle detection, visually-aided guidance, and a navigation system that generates collision-free kinematically feasible paths. The robot has seen extensive testing over numerous weeks of field trials during the day and night. The results in this paper pertain to one particular 3 h nighttime experiment in which the robot performed a coverage task and avoided obstacles. Additional results during the day demonstrate that the robot is able to continue operating during 5 min GPS outages by visually following crop rows. [ABSTRACT FROM AUTHOR]