The global navigation satellite systems (GNSS)/inertial navigation system (INS) integrated navigation system is widely used in the unmanned aerial vehicle (UAV) positioning. However, GNSS signals may ...be interrupted in complex environments such as signal shielding and occlusion, and the positioning accuracy will rapidly decrease under the independent INS system. To improve the positioning accuracy of UAV during GNSS outages, an error compensation method of GNSS/INS integrated navigation system assisted by long short-term memory (LSTM) neural network integrating attention mechanism (AT-LSTM) is proposed. When GNSS signals are available, the error compensation model between specific force, angular rate, INS attitude, and GNSS position increment is established. When GNSS signals are unavailable, the error compensation model outputs pseudo-GNSS signals to compensate for the integrated navigation system and suppress the divergence of positioning errors. The simulation experiment and actual experiment verify the performance of the error compensation method, and the experimental results show that the positioning accuracy of the AT-LSTM method is improved by more than 90% than that of INS within 60 s of GNSS outages, so the method can effectively improve the positioning accuracy of UAV during GNSS outages.
This paper proposes to use a newly-derived transformed inertial navigation system (INS) mechanization to fuse INS with other complementary navigation systems. Through formulating the attitude, ...velocity and position as one group state of group of double direct spatial isometries SE2(3), the transformed INS mechanization has proven to be group affine, which means that the corresponding vector error state model will be trajectory-independent. In order to make use of the transformed INS mechanization in inertial based integration, both the right and left vector error state models are derived. The INS/GPS and INS/Odometer integration are investigated as two representatives of inertial based integration. Some application aspects of the derived error state models in the two applications are presented, which include how to select the error state model, initialization of the SE2(3) based error state covariance and feedback correction corresponding to the error state definitions. Extensive Monte Carlo simulations and land vehicle experiments are conducted to evaluate the performance of the derived error state models. It is shown that the most striking superiority of using the derived error state models is their ability to handle the large initial attitude misalignments, which is just the result of log-linearity property of the derived error state models. Therefore, the derived error state models can be used in the so-called attitude alignment for the two applications. Moreover, the derived right error state-space model is also very preferred for long-endurance INS/Odometer integration due to the filtering consistency caused by its less dependence on the global state estimate.
Compared with the strapdown inertial navigation system (SINS), the rotation strapdown inertial navigation system (RSINS) can effectively improve the accuracy of navigation information, but rotational ...modulation also leads to an increase in the oscillation frequency of attitude errors. In this paper, a dual-inertial navigation scheme that combines the strapdown inertial navigation system and the dual-axis rotation inertial navigation system is proposed, which can effectively improve the attitude error accuracy in the horizontal direction by using the high-position information of the rotation inertial navigation system and the stability characteristics of the attitude error of the strapdown inertial navigation system. Firstly, the error characteristics of the strapdown inertial navigation system and the rotation strapdown inertial navigation system are analyzed, and then the combination scheme and Kalman filter are designed according to the error characteristics, and finally, the simulation experiment shows that the pitch angle error of the dual inertial navigation system is reduced by more than 35% and the roll angle error is reduced by more than 45% compared with the rotation strapdown inertial navigation system. Therefore, the combination scheme of double inertial navigation proposed in this paper can further reduce the attitude error of the rotation strapdown inertial navigation system, and at the same time, the two sets of inertial navigation systems can also enhance the reliability of ship navigation.
Zero velocity updates (ZUPT) is an effective way for the foot-mounted inertial pedestrian navigation systems. For the ZUPT technique to work properly, it is necessary to correctly detect the stance ...phase of each gait cycle. An adaptive stance-phase detection method is proposed based solely on an inertial sensor, which deals with the measurement fluctuations in swing and stance phases differently, and applies a clustering algorithm to partition the potential gait phases into true and false clusters, thereby yielding a time threshold to eliminate the false gait phases. The roles of the detection parameters and the relationship between them are analyzed to offer some suggestions for parameter tuning. Detection performance is evaluated with multisubject experimental data collected at varying walking speeds. The evaluation results show that the proposed detection method performs well in the presence of measurement fluctuations, which can make the detection of stance phases more robust and the choice of detection parameters more flexible.
Scene matching navigation system (SMNS) remains challenging in many navigation tasks, which rely heavily on accuracy, computational efficiency, and robustness. Due to the different generation ...conditions of the matching images, it is difficult for traditional methods to cover every aspect of the three navigation performances. This paper aims at developing an accurate, fast, and robust SMNS based on vision/inertial fusion to provide complete navigation information for unmanned aerial vehicles (UAVs). Utilizing the mechanization results of the low-cost MEMS, the proposed system first completes the georeferencing of the real-time aerial images, in which the projection errors are reduced greatly by introducing an optimized factor to the homography matrix. Then, applying a robust noise processing strategy, an improved feature extraction algorithm is designed to eliminate most of the features that vary with climate, time, and season, which lays a solid foundation for the accuracy of the following matching procedure. Under the framework of the SMNS, a novel matching strategy based on logic graphs is designed, which can facilitate the matching procedure. Eventually, by combining the mechanization results of the MEMS and the matching results of the SMNS, the proposed system can provide complete navigation results. Experiments in typical and complex scenarios are carried out respectively to verify the effectiveness and robustness of the proposed system. Experimental results demonstrate that the proposed SMNS possesses accuracy, computational efficiency, and robustness, which outperforms the state-of-the-art strategies(i.e., HOPC, CFOG, PC) in terms of matching aerial and satellite images.
A GPS-denied unmanned aerial vehicle (UAV) (Agent B) is localized through inertial navigation system alignment with the aid of a nearby GPS-equipped UAV (Agent A), which broadcasts its position at ...several time instants. Agent B measures the signals’ direction of arrival with respect to Agent B's inertial navigation frame. Semidefinite programming and the orthogonal Procrustes algorithm are employed, and accuracy is improved through maximum likelihood estimation. The method is validated using flight data and simulations. A three-agent extension is explored.
Before using the strap-down inertial navigation system (SINS)/celestial navigation system (CNS) integrated navigation system, the calibration of its error parameters is a necessary process to improve ...the system accuracy. In this article, an all-parameter system-level calibration method has been proposed, which utilizes Kalman filtering for simultaneously estimating the bias, scale factor, misalignments of inertial measurement unit (IMU), and installation errors of the star sensor. The observation has been constructed using the inertial navigation output information and the starlight vector measured by the star sensor. The observability of the error parameters has been analyzed theoretically, and the optimal excitation conditions for each error parameter are obtained. The effects of the angle between the starlight vector and the optical axis of the star sensor, and the attitude of the IMU, on the estimation accuracy of the installation error of the star sensor are also given. Finally, a ten-order rotation scheme has been designed, and all the error parameters can be optimally excited. The simulation and experimental results demonstrate that all the error parameters have better stability and repeatability, when compared with the traditional method, and the initial alignment error of <inline-formula> <tex-math notation="LaTeX">3.4^{\prime \prime } </tex-math></inline-formula> is better than <inline-formula> <tex-math notation="LaTeX">13.1^{\prime \prime } </tex-math></inline-formula> of the traditional method. Moreover, the installation error of the star sensor can be estimated with a high precision only when the angle between the starlight vector and the optical axis of the star sensor exceeds 1°. It is shown that the proposed method can effectively improve the navigation performance of the SINS/CNS integrated navigation system.
Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in ...the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.
Autonomous underwater vehicle (AUV) mostly relies on an integrated navigation system, which consists of a strap-down inertial navigation system (SINS) and Doppler velocity log (DVL). The integrated ...system provides continuous and accurate navigation information when compared to stand-alone SINS or DVL. However, the dependence of DVL signals on the acoustic environment may cause any DVL malfunction due to marine organisms or strong wave-absorbing material. This article introduces a novel method utilizing Dempster-Shafer (DS) theory augmented by least squares support vector machines (LSSVMs) known as DS-LSSVM. The SINS and DVL data fusion are designed by DS theory whereas LSSVM models the SINS error. The virtual DVL is built by the proposed DS-LSSVM method, which makes AUV navigation purposes possible during the long-term DVL outage. The test results demonstrate the effectiveness of the proposed virtual DVL signal estimation method. In addition, the positioning accuracy of the proposed method outperforms that of the LSSVM method.
This paper proposes a new quaternion calibration algorithm to calibrate large misalignment angles between Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL) in SINS/DVL ...integrated navigation system. A new SINS/DVL/GPS integrated navigation system is derived to complete the SINS alignment and SINS/DVL integrated navigation system calibration. Different from the traditional calibration model, the misalignment angles are described by quaternion, according to the physical properties of misalignment angle and scale factor error, the new measurement equation is derived: zero observation vector is used to estimate the misalignment angles, and velocity error scalar is used to estimate the scale factor error. A switching measurement information Kalman filter is designed to switch between different processes. The performance of the proposed quaternion calibration algorithm is evaluated through simulation comparisons with nonlinear model approaches and experiment. The simulation results demonstrate that the proposed quaternion calibration algorithm has better accuracy and robustness than the nonlinear model approaches. The experiment results show that the proposed quaternion calibration algorithm is effective, the SINS/DVL integrated navigation system positioning error after calibration is less than 1.65% mileage in more than 30km travel.
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