Integration Of Inertial Navigation System Research Articles

A hybrid information fusion method for SINS/GNSS integrated navigation system utilizing GRU-aided AKF during GNSS outages

Abstract To enhance the performance of integrated inertial navigation system (INS) and global navigation satellite system (GNSS) during GNSS outages, this paper proposed a fusion positioning method based on predictive observation information and adaptive filter parameter. Combined with an adaptive Kalman filter (AKF) and a Gated Recurrent Unit neural network (NN) that directly relates the inertial measurement unit (IMU) output sequence to the error estimation, the hybrid information fusion system can provide effective corrections to compensate for horizontal position errors under the constraints of complex and dynamic vehicle movement data during GNSS outages. Meanwhile, the designed adaptive parameter of the integrated navigation filter can adjust the credibility of the state prediction section when the GNSS is reconnected, ensuring the system can switch rapidly between the INS/GNSS and INS/NN integrated modes. The performance of the proposed information fusion method has been experimentally validated using IMU and GNSS data collected in a vehicle navigation test conducted on a stretch of expressway. The comparison results indicate that the proposed algorithm has error suppression capabilities under various experimental constraints and demonstrates a degree of extendibility and reusability.

LQR and LQG control of the helicopter during landing on the ship deck

PurposeThe purpose of this study is to test the performance of the designed automatic control system based on the Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) algorithms during landing of the helicopter on the ship deck. This paper is a further development of the series based on Topczewski et al. (2020).Design/methodology/approachThe system consists of two automatic control algorithms based on LQR and the LQG. It is integrated with the ship motion prediction system based on autoregressive algorithm with parameters calculated using Burg’s method. It is assumed that the source of necessary navigation data is integrated Inertial Navigation System with Global Positioning System. Landing of the helicopter on the ship deck is performed in automatic way, based on the preselected procedure. Performance of the control system is analyzed when all necessary navigation data is available for the system and in case when one of the parameters is unavailable during performing the procedure.FindingsIn this paper, description of the designed control system developed for performing the approach and landing of the helicopter using selected procedure is presented. Helicopter dynamic model is validated using the manufacturer data and by test pilots, overview is presented. Necessary information about ship motion model is also included. Tests showing mission performance while using LQR and LQG algorithms applied to the control system are presented and analyzed, taking into account both situations when full navigation data is available/unavailable for the control system.Practical implicationsResults of the system performance analyses can be used for selection of the proper control methodology for prospective helicopters autopilots. Furthermore, the system can be used to analyze the mission safety when information about one of the navigation parameters is identified by the navigation system as unavailable or incorrect and therefore unavailable during landing on the ship deck.Originality/valueIn this paper, control system dedicated for the automatic landing of the helicopter on the ship deck, based on two different control algorithms is presented. Influence of lack of information about one of the navigation parameters on the mission performance is analyzed.

Multi-Source Error Calibration and Observability Analysis of Integrated Inertial Navigation System/Polarization Sensor Navigation System

Accurate polarization information acquisition is the key technique of the integrated inertial navigation system (INS)/polarization sensor (PS) navigation system. To solve the problem of poor performance of polarization sensors that real-time error calibration by using a rotary table, we propose a fast calibration method that polarization sensor combined with inertial navigation systems. Firstly, we construct the calibration model of polarization sensor. A tightly coupled INS/PS calibration model is proposed, specifically, the polarization sensor errors, which are the intensity gain coefficient, the degree of polarization coefficient, and the installation angle error of a polarizer, are augmented into the state of the integrated navigation systems. By introducing the error term of polarization sensor into the state of the integrated navigation systems, the error estimation can be carried out and the speed of error calibration can be improved. Secondly, considering the nonlinearity of error calibration model, we use the Unscented Kalman Filter (UKF) to calibrate the error. On this basis, we make use of the observability analysis to verify its validity of the proposed model in a dynamic scene. Finally, real-world experiments are conducted on the self-developed multi-channel polarization sensor. The experimental results show that the calibration method can be done quickly without the use of a rotary table.

Investigation of the trade-off between the complexity of the accelerometer bias model and the state estimation accuracy in INS/GNSS integration

Abstract The integration of Inertial Navigation Systems and Global Navigation Satellite Systems (GNSS) represents the core navigation unit for mobile platforms in open sky environments. A realistic assessment of the accuracy of the navigation solution depends on the accurate modelling of inertial sensor errors. Sensor noise and biases contribute most to short-term navigation errors. For the latter, different models can be used, varying in complexity. This paper investigates how the use of two different models for the accelerometer bias affects the accuracy of the state estimate in an extended Kalman filter. For this purpose, the Allan variance technique is applied to a data sequence from a specific inertial sensor to identify and quantify the underlying noise processes. The estimated noise parameters are used to characterise a bias model for the accelerometers that in addition to the static bias model takes non-white noise processes of the inertial sensor under investigation into account. This detailed accelerometer bias model is compared to a classical modelling approach that only considers static biases. Both approaches are evaluated based on simulation studies for continuous and intermittent GNSS coverages. The results show no significant difference between the two modelling approaches in terms of horizontal position and attitude precision. Furthermore, the correctness of the accelerometer bias estimates is not significantly affected by the modelling approach. All in all, it can be concluded that a detailed bias model of the accelerometers does not outperform the classical modelling approach.

A Runs Test-Based Kalman Filter With Both Adaptability and Robustness With Application to INS/GNSS Integration

Kalman filter (KF) is one of the most efficient solutions applied for the integration of inertial navigation system (INS) and global navigation satellite system (GNSS). However, for standard KF derived from the ideal system model assumption, various disturbance factors inevitably decrease its estimation accuracy in practical scenarios. Therefore, it is necessary to suppress the interference factors such as inaccurate noise covariance or irregular process uncertainty during filtering, thus improving the integrated system’s stability and accuracy. Nevertheless, existing solutions usually target specific individual issues and lack compatibility with nonstationary scenarios in that multiple uncertain factors coexist. In this article, an improved KF with a switching scheme based on runs hypothesis test is designed to enhance the algorithm’s performance in nonstationary environments, and a process uncertainty robust filter based on mixture correntropy is proposed to suppress the potential diverge risks. By integrating with the variational Bayesian approach into the switching scheme, the noise covariance adaptability of the proposed scheme is further improved, and an improved KF with both adaptivity and robustness is obtained. The simulation and field test results demonstrate that the proposed filter achieved the expected superior performance in challenging nonstationary conditions.

Using a Moving Antenna to Improve GNSS/INS Integration Performance Under Low-Dynamic Scenarios

The integration of inertial navigation system (INS) and global navigation satellite system (GNSS) with a single antenna is widely applied in consumer-level vehicle navigation. However, it is challenging for a low-cost single-antenna GNSS/INS integrated system to provide reliable, consistent state estimation under low-dynamic scenarios (e.g., low-speed robotic applications) as the limited dynamics could lead to degradation of the system observability. We propose a novel moving-antenna GNSS/INS integration method, in which the antenna is designed to perform specific motions on the platform during vehicle maneuver, thus to improve the system observability in degraded conditions, bringing about the advantage of multi-antenna GNSS using a single antenna. It turns out that a moving antenna with a maximum speed of just 0.15 m/s would improve the system performance dramatically by applying raw GNSS carrier phase observations into the integration. In the proposed method, firstly, a moving-antenna fast initial alignment algorithm is developed, which enables the system to perform instantaneous initial alignment in static. Secondly, for GNSS/INS integration filter, the moving-antenna scheme improves the heading angle estimation accuracy by more than 50% under typical dynamic scenarios, achieving comparable performance to dual-antenna GNSS/INS integration. This work shows that it is possible and even beneficial to continuously change the inter-sensor transformation in an integrated navigation system.

A Novel Method for AI-Assisted INS/GNSS Navigation System Based on CNN-GRU and CKF during GNSS Outage

In the fields of positioning and navigation, the integrated inertial navigation system (INS)/global navigation satellite systems (GNSS) are frequently employed. Currently, high-precision INS typically utilizes fiber optic gyroscopes (FOGs) and quartz flexural accelerometers (QFAs) rather than MEMS sensors. But when GNSS signals are not available, the errors of high-precision INS also disperse rapidly, similar to MEMS-INS when GNSS signals would be unavailable for a long time, leading to a serious degradation of the navigation accuracy. This paper presents a new AI-assisted method for the integrated high-precision INS/GNSS navigation system. The position increments during GNSS outage are predicted by the convolutional neural network-gated recurrent unit (CNN-GRU). In the process, the CNN is utilized to quickly extract the multi-dimensional sequence features, and GRU is used to model the time series. In addition, a new real-time training strategy is proposed for practical application scenarios, where the duration of the GNSS outage time and the motion state information of the vehicle are taken into account in the training strategy. The real road test results verify that the proposed algorithm has the advantages of high prediction accuracy and high training efficiency.

Interference Mitigation Based on Low Complexity Subspace Tracking Method for Vehicle Positioning

Advanced driver assistance systems and automatic driving have drawn lots of attentions due to user’s multiapplication needs. Among different technologies, the integration of Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) is a key technique with the decreasing costs of Inertial Measurement Unit (IMU) and the increasing deployment of GNSS for navigation applications. Although the integration may have immune ability to interference in a certain extent, interference suppression strategy applied on the integration system can greatly improve the performance. A complete framework including interference monitor, detection, and mitigation is provided jointly using subspace tracking method and Kalman filter measurements innovation of INS and GNSS. The proposed monitor is simple, efficient, and can decide when to start the interference mitigation procedure, which can reduce the computation complexity significantly; Subspace-based method is introduced to track the interference basis matrix that can be feed to RootMUSIC to detect the frequency of interference, followed by the interference mitigation with a notch filter. The proposed framework has superior performance than the well-known time-frequency algorithm, such as short-time Fourier transform (STFT) and smoothed pseudo Wigner-Ville distribution (SPWVD), and can improve the performance of INS/GNSS system in the presence of interference with simulation verification.

Attitude determination in integrated GNSS-inertial navigation systems in information and measurement insufficiency conditions

The article presents the attitude determination algorithm for multi-antenna global navigation satellite and inertial integrated navigation system operating under information and measurement insufficiency conditions. The information and measurement insufficiency conditions are understood as the impossibility of the inertial system alignment on a fixed base and the absence of a redundant number of satellites to resolve the carrier phase integer ambiguity. The ability of the navigation system to operate in such conditions is achieved by combining several existing attitude determination methods and using the new method of attitude determination using ambiguous satellite phase measurements and absolute angular rate measurements. The hardware-in-the-loop simulation results using a 2-axis motion simulator and microelectromechanical systems inertial sensors are presented. The results confirm the efficiency of the proposed approach to the design of the navigation system algorithm.

Design of a dynamic positioning for unmanned surface vehicles using GPS/INS (VIAM-NAVI-M)

Today, Unmanned Surface Vehicles (USV) maintain the direction and fixed position necessary for many different applications such as security patrol, transmit information, water sampling, environmental monitoring... With USV model with two hull, control and propulsion systems, both of which are specifically designed to allow the vehicel to perform this task flexibly, omnidirectional and maneuverable. With environmental effects, such as wind, waves and currents ..., it has a large impact on ships, leading to large errors or fluctuations. Therefore, a controller designed to produce better performance for USV under changing noise conditions is essential. To improve the ability of navigation for vehicles, Viam-Navi-M GPS/INS Module: integration of Inertial Navigation System (INS) and Global Positioning System (GPS) is developed with low-cost, highly accurate and stable navigation system. At the same time, the article will present the process of system development and software architecture design. Finally, with the four engine and controller propulsion system built and tested, it shows that the boat is well controlled, its ability to maintain specific direction and position for long periods of time. The postion error is maintained less than 1 meter most of the experimental time and the heading error is between -5 and +5 degrees.

Adaptive Central Difference Kalman Filter With Unknown Measurement Noise Covariance and Its Application to Airborne POS

Position and Orientation system (POS), a loosely integrated inertial navigation system (INS) and global positioning system (GPS), can provide high-accuracy motion information for the airborne remote sensing loads, which plays a crucial role in airborne remote sensing imaging. However, the airborne POS often suffers from the harsh environment, such as aircraft maneuver mode and other external disturbance, which will lead to measurement noise unknown and further affects the accuracy of motion parameters. In this paper, an adaptive central difference Kalman filter method based on expectation maximization algorithm is proposed, which can estimate measurement noise adaptively and further improve the performance of POS. A flight experiment is conducted and the results show that the proposed method achieves higher-accuracy motion information by compared with the traditional CDKF method and covariance matching.

Degeneration-Aware Outlier Mitigation for Visual Inertial Integrated Navigation System in Urban Canyons

In this article, we proposed a graduated nonconvexity (GNC) aided outlier mitigation method for the improvement of the visual-inertial integrated navigation system (VINS) to face the challenge of dynamic environments with numerous unexpected outlier measurements. A GNC optical flow algorithm was proposed for the detection of the outliers of feature tracking in the front-end of VINS by iteratively estimating the optical flow and the optimal weightings of feature correspondences. Then the feature correspondences with small weightings were excluded. However, excessive outlier exclusion may cause insufficient constraints on the state, causing degeneration of VINS. To solve the problem, this article proposed to detect the potential degeneration based on the degree of constraint in different directions of the pose estimation. Then the number of features being considered was intelligently adapted based on the degeneration level to improve the geometry constraint in the coming epochs. We evaluated the effectiveness of the proposed method by using two challenging datasets (including challenging night scenarios) collected in urban canyons of Hong Kong. The results show that the proposed method can effectively reject the potential outlier visual measurements, and alleviate the degeneration, leading to improved positioning performance in both evaluated datasets.

Star Centroid Positioning Error Correction Aided by Gyroscope Output in INS and CNS

AbstractIn a traditional integrated inertial navigation system (INS) and celestial navigation system (CNS) setup, measurements from both systems are only fused at the data output phase. Navigation ...

Integrated Inertial Navigation System with camera-based system using LabVIEW Software

Integrated Inertial Navigation System with camera-based system using LabVIEW Software

INS/CNS Integrated Navigation Based on Corrected Infrared Earth Measurement

In the integrated inertial navigation system (INS) and celestial navigation system (CNS), the attitudes and positions of probes are estimated based on the auxiliary measurement of an infrared Earth sensor. The poor accuracy of the infrared Earth sensor compared to that of the star sensor significantly limits the navigation accuracy of the integrated navigation system. This paper proposes a novel INS/CNS integrated navigation system that uses the specific force with noncumulative error to correct infrared Earth measurements. The probe azimuth angle and elevation angle relative to the Earth's inertial frame are first estimated according to the specific force provided by the INS to filter the infrared Earth sensor's measurement error. Then, the corrected infrared Earth measurement assists the CNS to obtain accurate celestial measurements. The integrated INS/CNS navigation system in the tightly coupled mode finally estimates the accurate probe attitude and position. Compared to the integrated navigation system, which directly uses the uncorrected infrared Earth measurement, the yaw, pitch, and roll angle are improved under the proposed method by 76.73%, 77.64%, and 75.39% on average, respectively; the latitude and longitude estimation errors improve under the proposed method by 70.44% and 61.92% on average, respectively. In short, the proposed method is a feasible and highly accurate approach to the INS/CNS integrated navigation system.

A New Velocity Meter Based on Hall Effect Sensors for UAV Indoor Navigation

This paper presents a new approach to enhance the indoor navigation of unmanned aerial vehicles (UAVs). During indoor flight missions, UAVs rely on the integration of inertial navigation systems (INS) and aiding sensors measurements to estimate the UAV’s navigation states. These aiding measurements may be position, velocity, or attitudes. For indoor environments, the presence of absolute positioning systems, such as Wi-Fi, as an aiding sensor is not always guaranteed. Therefore, alternative aiding sources are required to mitigate the drift exhibited in INS and enhance the navigation accuracy. Different sensors have been utilized to aid INS. These aiding sensors should be carefully chosen, as they should not exceed the cost, weight, size, and power consumption limitations of the UAV. This paper presents a new utilization of Hall effect sensors to act as a flying air odometer (Air-Odo) for quadcopter UAV and to aid the INS with velocity update. Testing results confirmed the capabilities of the new approach to aid the navigation solution through real experiments, with velocity update applied to an extended Kalman filter. The new approach enhanced the navigation solution by 98% when compared with the stand-alone INS solution.

Integration of Inertial Navigation System with EM-log Using H-infinity Filter

This paper presents the integration of inertial navigation system (INS) with electromagnetic-log (EM-log) as an underwater navigation system using H-infinity filter for robustness from the uncertainty of the sea current model. In underwater environments, the electromagnetic signals are attenuated rapidly, so that the global navigation satellite system is not available in general. Thus, INS is usually chosen for underwater navigation, and other aiding sensors are also used to complement its accumulative errors, one of which is EM-log. Since an EM-log provides the relative velocity to seawater, the integrated navigation cannot be performed accurately unless the sea current speed is compensated properly. Generally, the INS and EM-log can be integrated using extended Kalman filter (EKF). However, EKF guarantees its performance when the stochastic properties of the system’s process and measurement noises are perfectly known. In other words, in the presence of sea current modelling errors, the integration using the EKF is not expected to show good performance. On the other hand, H-infinity filter is a robust filter which can tolerate such uncertainties. In this paper, the integration of INS and EM-log using H-infinity filter is studied. The performance is compared with that of the EKF case by proper computer simulation.

A New Approach to Estimate True Position of Unmanned Aerial Vehicles in an INS/GPS Integration System in GPS Spoofing Attack Conditions

This paper presents a new approach to estimate the true position of an unmanned aerial vehicle (UAV) in the conditions of spoofing attacks on global positioning system (GPS) receivers. This approach consists of two phases, the spoofing detection phase which is accomplished by hypothesis test and the trajectory estimation phase which is carried out by applying the adapted particle filters to the integrated inertial navigation system (INS) and GPS. Due to nonlinearity and unfavorable impacts of spoofing signals on GPS receivers, deviation in position calculation is modeled as a cumulative uniform error. This paper also presents a procedure of applying adapted particle swarm optimization filter (PSOF) to the INS/GPS integration system as an estimator to compensate spoofing attacks. Due to memory based nature of PSOF and benefits of each particle’s experiences, application of PSOF algorithm in the INS/GPS integration system leads to more precise positioning compared with general particle filter (PF) and adaptive unscented particle filer (AUPF) in the GPS spoofing attack scenarios. Simulation results show that the adapted PSOF algorithm is more reliable and accurate in estimating the true position of UAV in the condition of spoofing attacks. The validation of the proposed method is done by root mean square error (RMSE) test.

Adaptive In-Flight Alignment of INS/GPS Systems for Aerial Mapping

In this paper, a new in-flight alignment (IFA) method of the integrated inertial navigation system (INS) and global positioning system (GPS) is proposed for the aerial mapping applications. The integrated INS/GPS measurement system is used to provide attitude information and, based on this, the exterior orientation parameters can be derived for the purpose of direct georeference of the airborne imagery. The IFA plays an important role in achieving high accuracy of attitude estimation. However, the statistics of INS noise is usually time-varying and seriously degrades the estimation accuracy in practice. In order to solve the problem, two strategies are taken into account in the paper. First, an adaptive estimation algorithm is developed by adjusting the window size of data processing in IFA, so that the covariances of INS noise can be estimated and updated online to improve the state estimation performance. Second, a strong tracking filter is applied to guarantee the convergence of the IFA algorithm as well as its robustness against parameter perturbations and trajectory maneuvers. Finally, an aerial mapping experiment is implemented, and the results demonstrate the effectiveness of the proposed method.

A novel intelligent adaptive Kalman Filter for estimating the Submarine's velocity: With experimental evaluation

In this paper, an intelligent adaptive algorithm based on the integrated Inertial Navigation System (INS) is presented for estimating the velocity of an Autonomous Underwater Vehicle (AUV). The most common algorithm for incorporating the navigation data is Kalman Filter (KF). In the conventional KF algorithm, the covariance matrix of measurement noise is considered as a constant through running time. The noise covariance in the Adaptive Kalman Filter (AKF), is estimated through processing the innovation sequence inside a window of constant length. In actual conditions, the correct window length to achieve the best estimation, depends on the operational conditions. In this paper, proper window length in the adaptive algorithm is estimated by processing the probability density function of the innovation sequence at each time step; so the window length in the proposed algorithm is variable due to condition changes. The proposed integrated navigation system consists of a three-axis Inertial Measurement Unit (IMU) and a three-axis Doppler Velocity Log (DVL). The performance of the proposed system is evaluated through four sea tests using an AUV. Experimental results show that the proposed system has superior performance than the conventional KF algorithm and is similar to the optimum AKF(AKF with the best window length).