Improvement In Navigation Accuracy Research Articles

INS aided spoofing detection of high dynamic GNSS receiver for launch vehicle applications: A loosely coupled approach

Launch vehicle applications require highly accurate and reliable navigation systems that can withstand the high dynamic conditions of the launch and flight phases. GNSS-aided INS (Inertial Navigation System) is the most popular solution for launch vehicle navigation. However, the accuracy of Global Navigation Satellite System (GNSS) receivers can be compromised by malicious spoofing attacks, which can result in significant safety risks and mission failure. This paper presents a method for identifying and mitigating the spoofing attacks on Global Navigation Satellite System (GNSS) receivers using the independent and simultaneous measurements of the Inertial Navigation System (INS). A state space approach is attempted in this paper with an extended Kalman Filter for identifying the spoofed signals. The well-established method of integrating INS and GNSS data for navigation accuracy improvement is taken as the baseline of this algorithm. The proposed method is an extension of a loosely coupled integration algorithm already established for fusing INS and GNSS data. The extended Kalman filter estimated position and velocity of the receiver is used along with the satellite position and velocity computed from ephemeris to find out the range and range rate of each of the satellites to the receiver. The difference between the estimated range and range rate against the measured range and range rate is considered as filter residual. The filter residuals are taken as the parameter to identify the spoofing. The threshold of filter residuals for range and range rates is the critical parameter that decides the false isolation and missing detection. The threshold is decided based on the environment in which the receiver is operated, the dynamics expected, and the noise model. The algorithms envelopment and simulation studies are done. The simulation tests are completed using a GNSS constellation simulator with spoofed signals. The INS and GNSS error model, states selection, Kalman filter design, algorithm details, simulation tests, simulation results, advantages and disadvantages of the new algorithm, and future work are covered in this paper.

In-Flight Performance Assessment of the Single-Frequency GPS/BDS Receiver for Yaogan-30 Series Satellites Real-Time Navigation

Within the Yaogan-30 series satellites, the low-cost single-frequency Global Positioning System/BeiDou Navigation Satellite System (GPS/BDS) receiver was employed for onboard real-time navigation. This article presents the in-flight performance of the single-frequency GPS/BDS receiver, including the tracking of GPS/BDS satellites, the noise of measurements, and the orbit accuracy of onboard real-time navigation results. First, in terms of satellites tracking, no BDS-2 satellites are tracked in 30–40% of all the time, four to seven BDS-2 satellites can be observed for less than 15% of the time, and only one to three BDS-2 satellites are available for the rest time, whereas 7–12 GPS satellites are tracked for the vast majority of the time. Obviously, the GPS observations play a more important role than BDS-2 data in real-time navigation and precise orbit determination (POD). Although the receivers only provide the single-frequency C/A-code pseudorange measurements without carrier-phases output, the accuracies of postprocessed POD could arrive at the level of 10–25 cm in terms of 3-D position. Hence, the post precise orbits are used as the reference for the assessment of real-time navigation accuracy. The assessment results demonstrate that the onboard real-time navigation accuracies of 2.0–3.5 m for 3-D position and 10.0–13.0 mm/s for 3-D velocity are achievable. At the same time, the statistics on the C/A-code pseudorange residuals after POD processing reflects that the noises of measurements from GPS and BDS-2 satellites are at the same level of 0.5–1.6 m. It should be noted that there is a notable velocity deviation of up to about 7.6 mm/s. Nevertheless, even with this obvious systematic error, the current velocity accuracy could still meet the real-time requirements of the small Yaogan-30 series satellites. In addition, some offline simulative solutions with tuned strategy settings are tested to validate the potential real-time navigation accuracy improvement. The simulation experiment tests results illustrate that a higher real-time orbit accuracy of roughly 1.0 m for 3-D position and 1.0 mm/s for 3-D velocity could be obtainable, if an optimized power spectral density of estimated empirical accelerations and a slightly higher order and degree of gravity model (50×50) are adopted. All the above analyses show that the single-frequency pseudorange Global Navigation Satellite System receiver is a high cost-effective sensor and has a wide horizon of application in the aerospace market.

ZUPT-Aided INS Bypassing Stance Phase Detection by Using Foot-Instability-Based Adaptive Covariance

In this paper, we propose a Foot-Instability-Based Adaptive (FIBA) covariance to dynamically adjust the covariance matrix for the pseudo-zero-velocity measurements in the Zero velocity UPdaTe (ZUPT)-aided Inertial Navigation Systems (INS). The proposed ZUPT-aided INS using the FIBA covariance is implemented in an Extended Kalman Filter (EKF) framework, which utilizes a time-varying measurement covariance matrix that is updated in each iteration according to the FIBA covariance. The FIBA covariance is designed to have a very high value during the swing phases in a gait cycle, and the value significantly decreases during the stance phases. As a result, the proposed method eliminates a need to use a binary stance phase detector in implementation of the ZUPT-aided INS. Properties of EKF innovation sequences in the algorithm were studied, and two series of indoor pedestrian navigation experiments were conducted to demonstrate the navigation performance of the proposed system. In the first series of experiments, which included cases of walking and running, localization solutions produced by the system using the FIBA covariance demonstrated 36% and 64% improvements in navigation accuracy along the horizontal and vertical directions, respectively. In the second series of experiments, which included a pedestrian walking on different indoor terrains, such as flat planes, stairs, and ramps, the navigation accuracy of the system using the FIBA covariance reduced horizontal and vertical position errors by 12% and 45%, respectively, as compared to the conventional ZUPT-aided INS.

Impact of augmented-reality improvement in ablation catheter navigation as assessed by virtual-heart simulations of ventricular tachycardia ablation

BackgroundRecently, an augmented reality (AR) solution allows the physician to place the ablation catheter at the designated lesion site more accurately during cardiac electrophysiology studies. The improvement in navigation accuracy may positively affect ventricular tachycardia (VT) ablation termination, however assessment of this in the clinic would be difficult. Novel personalized virtual heart technology enables non-invasive identification of optimal lesion targets for infarct-related VT. This study aims to evaluate the potential impact of such catheter navigation accuracy improvement in virtual VT ablations. Methods2 MRI-based virtual hearts with 2 in silico induced VTs (VT 1, VT 2) were included. VTs were terminated with virtual “ground truth” endocardial ablation lesions. 106 navigation error values that were previously assessed in a clinical study evaluating the improvement of ablation catheter navigation accuracy guided with AR (53 with, 53 without) were used to displace the “ground truth” ablation targets. The corresponding ablations were simulated based on these errors and VT termination for each simulation was assessed. ResultsIn 54 VT 1 ablation simulations, smaller error with AR significantly resulted in more VT termination (25) compared to the error without AR (16) (P < 0.01). In 52 VT 2 ablation simulations, no significant difference was observed from error with (11) and without AR (13) (P = 0.58). The substrate characteristic may impact the effect of improved accuracy to an improved VT termination. ConclusionVirtual heart shows that the increased catheter navigation accuracy provided by AR guidance can affect the VT termination.

Integrated celestial navigation for spacecraft using interferometer and Earth sensor

An integrated celestial navigation scheme for spacecrafts based on an optical interferometer and an ultraviolet Earth sensor is presented in this paper. The optical interferometer is adopted to measure the change in inter-star angles due to stellar aberration, which provides information on the velocity of the spacecraft in the plane perpendicular to the direction of the observed star. In order to enhance the navigation performance, the measurements obtained from the ultraviolet Earth sensor is used to eliminate the unfavorable effect caused by the gravitational deflection of starlight. As the prior knowledge about the optical path delay bias of the optical interferometer may be ambiguous, a Q-learning extended Kalman filter is derived to fuse the two types of measurements, and estimate the kinematic state together with the optical path delay bias. The solution of the autonomous navigation system consists of position, velocity and attitude of the spacecraft. Numerical simulation shows that an evident improvement in navigation accuracy can be achieved by introducing the ultraviolet Earth sensor into the navigation system. In addition, it is shown that the Q-learning extended Kalman filter performs better than the traditional extended Kalman filter.

A Novel Rotation Scheme for MEMS IMU Error Mitigation Based on a Missile-Borne Rotation Semi-Strapdown Inertial Navigation System.

In previous research, a semi-strapdown inertial navigation system (SSINS), based on micro-electro-mechanical system (MEMS) sensors, was able to realize over-range measurement of the attitude information of high-rotation missiles by constructing a single axis “spin reduction” platform. However, the MEMS sensors in SSINS were corrupted by significant sensor errors. In order to further improve SSINS measurement accuracy, a rotational modulation technique has been introduced to compensate for sensor errors. The ideal modulation angular velocity is changed sharply to achieve a constant speed, while in practical applications, the angular rate of the rotating mechanism’s output needs to go through an acceleration-deceleration process. Furthermore, the stability of the modulation angular rate is difficult to achieve in a high-speed rotation environment. In this paper, a novel rotation scheme is proposed which can effectively suppress the residual error in the navigation coordinate system caused by the modulation angular rate error, including the acceleration-deceleration process and instability of angular rate. The experiment results show that the position and attitude accuracy of the new rotation scheme was increased by more than 56%. In addition, the proposed scheme is applicable to navigation accuracy improvement under various dynamic conditions.

Seamless global positioning system/inertial navigation system navigation method based on square-root cubature Kalman filter and random forest regression.

In this paper, a seamless navigation dual-model based on Square-Root Cubature Kalman Filter (SRCKF) and Random Forest Regression (RFR) is developed to enhance the performance of the Global Positioning System (GPS)/Inertial Navigation System (INS) integrated navigation system. By using the proposed method, the system can ensure seamless navigation ability even during GPS signal outages. In the proposed dual-model, sub-model 1 that directly relates the specific force of INS to the measurement of filter and sub-model 2 that directly relates the cubature points and innovation of SRCKF to the error caused by filter are established. Combined with SRCKF and RFR algorithms, the dual-model system can predict and estimate the velocity and position of the vehicle seamlessly when GPS signals are blocked. Field test data are collected to evaluate the proposed solution, and the experimental results show that the model proposed has obvious improvement in navigation accuracy by comparison. The prominent advantages of the proposed seamless navigation method include the following: (i) the proposed dual-model can effectively provide corrections to standalone INS during GPS outages, which outperforms traditional widely used single model; (ii) the proposed combination of SRCKF and RFR achieves better performance in the prediction of INS errors than other combination algorithms.

A high-accuracy constrained SINS/CNS tight integrated navigation for high-orbit automated transfer vehicles

High-accuracy and reliable autonomous navigation is increasingly crucial for automated transfer vehicles (ATV). This paper proposes a novel strapdown inertial navigation system/celestial navigation system (SINS/CNS) tight integration scheme aided by dynamic model constraints for high-orbit ATV to realize accurate and autonomous navigation. In this scheme, the complete weightlessness constraint in orbit is used to address the divergence of position and velocity caused by inaccurate accelerometer bias estimation problem encountered in the traditional SINS/CNS integration method, and the image point position-based tight integration model is derived to handle the adverse influence of time-varying attitude measurement noise due to changes of star geometry observed by a large-view-filed star sensor. Moreover, an information filter is devised to fuse the multi-rate measurements. The proposed algorithm is evaluated by a representative high-orbit ATV trajectory simulation, which indicates significant improvements in navigation accuracy compared with its traditional counterparts. The proposed algorithm can realize navigation accuracy enhancements without introducing additional sensors, strengthening its potentials in engineering application.

Analysis and Calibration of the Nonorthogonal Angle in Dual-Axis Rotational INS

Navigation errors of inertial navigation system can be effectively restrained by rotating inertial measurement units (IMU), enhancing accuracy in long-endurance navigation. However, nonorthogonal angles between rotating axes are inevitable during manufacturing. Coupled into the navigation error, nonorthogonal angles can lead to navigation accuracy, especially attitude accuracy decline during rotation. Therefore, the calibration and compensation of nonorthogonal angles will enhance the systemic reliability. In this study, we first established nonorthogonal angles model, then estimated the effect on sensitive errors of IMU using the proposed model. Based on the relationship between nonorthogonal angles and IMU attitude, an improved calibration algorithm of estimating nonorthogonal angles is presented. The results of theoretical analysis demonstrated that this method can effectively calibrate nonorthogonal angles in a simple and fast way, which is desirable for practical requirements. Simulation and comparison analysis indicated that the calibration of nonorthogonal angles is effective and practical. Finally, the feasibility of calibration and compensation is also verified by the improvement of navigation accuracy in the experiment.

Improvement of Navigation Accuracy using Tightly Coupled Kalman Filter

In this paper, a mechanism is designed for integration of inertial navigation system information (INS) and global positioning system information (GPS). In this type of system a series of mathematical and filtering algorithms with Tightly Coupled techniques with several objectives such as application of integrated navigation algorithms, precise calculation of flying object position, speed and attitudes at any moment. Obviously, GPS speed information will contribute to make better estimates of the state in addition to location information. Typically, Kalman filter provides optimal method for states estimation and also creates possibility of combining several measurements to acquiesce a united estimate of system status.Tightly Coupled Kalman filter is a novel and applicable approach to effectively track path with high accuracy especially when four satellites are not available or satellite system stops along the route. Indeed, an important advantage of integration with Tightly Coupled filtration is related to application of software system rather than hardware which somehow reduces hardware complexity and also other advantages of sensors integration is associated with application of all benefits of various sensors as well as covering their individual imperfections in order to increase navigation accuracy. Generally, in integration systems exact GPS observations are used to estimate and INS errors modification by Kalman filter. It is expected that an integrated system with high-precision provides an accurate estimation of all unknowns’ parameters and states through kalman filter. Simulations executed for integrated navigation system demonstrate that a flying object could sufficiently compensate errors resulting from modeling of inertial navigation that grows integrally over time and also impressively inhibit flying object deviation respect to condition that only GPS location information are available.

Autonomous Vehicle Dynamic Model-Based Navigation for Small UAVs

This paper presents a novel approach to autonomous navigation for small UAVs, with no extra sensor added to the conventional INS/GNSS setup. The proposed method significantly increases the accuracy and reliability of autonomous navigation, especially for small UAVs with low-cost IMUs. This improvement is of special interest in the case of GNSS outages, where inertial coasting drifts very quickly. In the proposed architecture, the VDM provides the estimate of position, velocity, and attitude, which is updated within a filter based on available observations, such as IMU data or when available, GNSS measurements. The filter is capable of estimating wind velocity and dynamic model parameters, in addition to navigation states and IMU sensor errors. Monte Carlo simulations reveal major improvements in navigation accuracy compared to conventional INS/GNSS systems during GNSS outages of 5 min. A discussion on the observability is also presented at the end. Copyright (C) 2016 Institute of Navigation.

AUTONOMOUS NAVIGATION OF SMALL UAVS BASED ON VEHICLE DYNAMIC MODEL

This paper presents a novel approach to autonomous navigation for small UAVs, in which the vehicle dynamic model (VDM) serves as the main process model within the navigation filter. The proposed method significantly increases the accuracy and reliability of autonomous navigation, especially for small UAVs with low-cost IMUs on-board. This is achieved with no extra sensor added to the conventional INS/GNSS setup. This improvement is of special interest in case of GNSS outages, where inertial coasting drifts very quickly. In the proposed architecture, the solution to VDM equations provides the estimate of position, velocity, and attitude, which is updated within the navigation filter based on available observations, such as IMU data or GNSS measurements. The VDM is also fed with the control input to the UAV, which is available within the control/autopilot system. The filter is capable of estimating wind velocity and dynamic model parameters, in addition to navigation states and IMU sensor errors. Monte Carlo simulations reveal major improvements in navigation accuracy compared to conventional INS/GNSS navigation system during the autonomous phase, when satellite signals are not available due to physical obstruction or electromagnetic interference for example. In case of GNSS outages of a few minutes, position and attitude accuracy experiences improvements of orders of magnitude compared to inertial coasting. It means that during such scenario, the position-velocity-attitude (PVA) determination is sufficiently accurate to navigate the UAV to a home position without any signal that depends on vehicle environment.

AUTONOMOUS NAVIGATION OF SMALL UAVS BASED ON VEHICLE DYNAMIC MODEL

Abstract. This paper presents a novel approach to autonomous navigation for small UAVs, in which the vehicle dynamic model (VDM) serves as the main process model within the navigation filter. The proposed method significantly increases the accuracy and reliability of autonomous navigation, especially for small UAVs with low-cost IMUs on-board. This is achieved with no extra sensor added to the conventional INS/GNSS setup. This improvement is of special interest in case of GNSS outages, where inertial coasting drifts very quickly. In the proposed architecture, the solution to VDM equations provides the estimate of position, velocity, and attitude, which is updated within the navigation filter based on available observations, such as IMU data or GNSS measurements. The VDM is also fed with the control input to the UAV, which is available within the control/autopilot system. The filter is capable of estimating wind velocity and dynamic model parameters, in addition to navigation states and IMU sensor errors. Monte Carlo simulations reveal major improvements in navigation accuracy compared to conventional INS/GNSS navigation system during the autonomous phase, when satellite signals are not available due to physical obstruction or electromagnetic interference for example. In case of GNSS outages of a few minutes, position and attitude accuracy experiences improvements of orders of magnitude compared to inertial coasting. It means that during such scenario, the position-velocity-attitude (PVA) determination is sufficiently accurate to navigate the UAV to a home position without any signal that depends on vehicle environment.

A new tightly-coupled INS/CNS integrated navigation algorithm with weighted multi-stars observations

Integrating celestial navigation system (CNS) and inertial navigation system (INS) is an effective way to realize autonomous navigation for hypersonic cruise vehicles. In multi-stars synchronous observation, the error levels of starlight observations are not only changing with time, but also different from star to star, which is ignored by classic INS/CNS integrated navigation. In this paper, a new tightly-coupled INS/CNS integrated navigation algorithm with weighted multi-stars observations is proposed. Different from the classic method, tightly-coupled model provides a novel framework for INS/CNS integration, which enables direct fusion of INS measurements with original CNS observations. Meanwhile, star subsets traversal strategy is designed to evaluate the error level of each observation. Then adaptive filter which can fuse multi-star observations with INS according to evaluated error levels is researched. The proposed new algorithm shows significant improvement in navigation accuracy than the classic method in simulation.

Research on Anti-Windup Controller of Autonomous Navigation Vehicle Based on Improved Adaptive Filter

This paper presents relevant methods on navigation accuracy improvement of agricultural vehicle focusing on positioning accuracy and control precision. An adaptive kalman filtering, combination of Sage_Husa adaptive filtering and strong tracking kalman filtering based on strict convergence criterion, is adopted to improve filtering accuracy with strong ability of adaptive filtering and restraining filter divergence. A new variable-structure switching method to prevent PID controller from integrator windup can effectively solve the integral saturation phenomenon, which adopts a kind of adaptive adjustment rate to adjust the integral term of PID control algorithm. Finally, this paper puts the improved adaptive filtering and anti-windup variable-structure PID control technique into combination to effectively restrain interference and integral saturation, so as to achieve the purpose of improving system stability and control precision. The simulation and experiment results show that methods described above greatly enhance the capabilities of restraining filtering divergence and improving control precision.

A new source difference artificial neural network for enhanced positioning accuracy

Integrated inertial navigation system (INS) and global positioning system (GPS) units provide reliable navigation solution compared to standalone INS or GPS. Traditional Kalman filter-based INS/GPS integration schemes have several inadequacies related to sensor error model and immunity to noise. Alternatively, multi-layer perceptron (MLP) neural networks with three layers have been implemented to improve the position accuracy of the integrated system. However, MLP neural networks show poor accuracy for low-cost INS because of the large inherent sensor errors. For the first time the paper demonstrates the use of knowledge-based source difference artificial neural network (SDANN) to improve navigation performance of low-cost sensor, with or without external aiding sources. Unlike the conventional MLP or artificial neural networks (ANN), the structure of SDANN consists of two MLP neural networks called the coarse model and the difference model. The coarse model learns the input–output data relationship whereas the difference model adds knowledge to the system and fine-tunes the coarse model output by learning the associated training or estimation error. Our proposed SDANN model illustrated a significant improvement in navigation accuracy of up to 81% over conventional MLP. The results demonstrate that the proposed SDANN method is effective for GPS/INS integration schemes using low-cost inertial sensors, with and without GPS.

Cooperative Localization Method for AUV Using Acoustic Communication Measurement

In this paper, we propose an algorithm based on double acoustic measurement for cooperative navigation of multiple autonomous underwater vehicles. Research on cooperative navigation of AUV is an important topic to solve the navigation problem in long range and deep sea. We investigate the improvement in navigation accuracy. In the Leader-follow structure, the leader AUV is equipped with high precision navigation system, and the follow AUV is equipped with low precision navigation system. They all are equipped with acoustic device to measure relative location. Traditionally geometry triangulation method is used to calculate the position of follow AUV, the method may cause fault error solution. Double acoustic communication measurement method was designed, which fused the proprioceptive and exteroceptive sensors. The research results prove that the navigation accuracy has been improved effectively.

Error Analysis of Strapdown Inertia Navigation System in Tactical Missiles

To clarify the navigation error of strapdown inertial navigation system(SINS) with low navigation accuracy, short working period, and fickle operating environment that installed in tactical missiles, a new navigation error model of SINS was established. Founded by a six dimensional kinematic model of tactical missile and a three dimensional target model, the paper rebuilt the whole course of tactical missile attacking the moving target, where correlative output parameters were used to calculate the navigation error model of SINS simultaneously. Experiment result revealed the influence of different error sources to navigation errors of SINS, which is constructive to the improvement of navigation accuracy of SINS.

Cooperative navigation and localization for multiple UUVs

The authors proposed a moving long baseline algorithm based on the extended Kalman filter (EKF) for cooperative navigation and localization of multi-unmanned underwater vehicles (UUVs). Research on cooperative navigation and localization for multi-UUVs is important to solve navigation problems that restrict long and deep excursions. The authors investigated improvements in navigation accuracy. In the moving long base line (MLBL) structure, the master UUV is equipped with a high precision navigation system as a node of the moving long baseline, and the slave UUV is equipped with a low precision navigation system. They are both equipped with acoustic devices to measure relative location. Using traditional triangulation methods to calculate the position of the slave UUV may cause a faulty solution. An EKF was designed to solve this, combining the proprioceptive and exteroceptive sensors. Research results proved that the navigational accuracy is improved significantly with the MLBL method based on EKF.

A combined algorithm of improving INS error modeling and sensor measurements for accurate INS/GPS navigation

Although the integrated system of a differential global positioning system (DGPS) and an inertial navigation system (INS) had been widely used in many geodetic navigation applications, it has sometimes a major limitation. This limitation is associated with the frequent occurrence of DGPS outages caused by GPS signal blockages in certain situations (urban areas, high trees, tunnels, etc.). In the standard mechanization of INS/DGPS navigation, the DGPS is used for positioning while the INS is used for attitude determination. In case of GPS signal blockages, positioning is provided using the INS instead of the GPS until satellite signals are obtained again with sufficient accuracy. Since the INS has a very short-time accuracy, the accuracy of the provided INS navigation parameters during these periods decreases with time. However, the obtained accuracy in these cases is totally dependent on the INS error model and on the quality of the INS sensor data. Therefore, enhanced navigation parameters could be obtained during DGPS outages if better inertial error models are implemented and better quality inertial measurements are used. In this paper, it will be shown that better INS error models are obtained using autoregressive processes for modeling inertial sensor errors instead of Gauss–Markov processes that are implemented in most of the current inertial systems and, on the other hand, that the quality of inertial data is improved using wavelet multi-resolution techniques. The above two methods are discussed and then a combined algorithm of both techniques is applied. The performance of each method as well as of the combined algorithm is analyzed using land-vehicle INS/DGPS data with induced DGPS outage periods. In addition to the considerable navigation accuracy improvement obtained from each single method, the results showed that the combined algorithm is better than both methods by more than 30%.