Satellite/Inertial Navigation Integrated Navigation Method Based on Improved Kalman Filtering Algorithm

Asteroid exploration missions demand more autonomous navigation system that are precise and in real-time. Visual-aided inertial navigation systems combine the advantages of autonomy of inertial navigation systems and accuracy of visual navigation systems. However, both increase the computational burden for the information processing. With the goal of decreasing the computational load, this paper proposes a low cost vision-aided inertial navigation method which can calculate increment position, velocity and attitude directly in inertial frame by pre-integrating IMU measurements. For visual information, specific combinations of the features are selected according to information entropy theory to reduce computation. Simulation results show that the standard deviations of the spacecraft’s estimated position error is less than 1 m, the velocity less than 0.002 m/s and the attitude angle less than 0.005 deg, compatible with precision landing on asteroids.

PurposeBefore designing a navigation system, it is necessary to analyse possible approaches in terms of expected accuracy, existing limitations and economic justification to select the most advantageous solution. This paper aims to collect possible navigation methods that can provide correction for inertial navigation and to evaluate their suitability for use on a manoeuvring tactical missile.Design/methodology/approachThe review of existing munitions was based on data collected from the literature and online databases. The data collected included dimensions, performance, applied navigation and guidance methods and their achievable accuracy. The requirements and limitations identified were confronted with the range of sensor parameters available on the market. Based on recent literature, navigation methods were reviewed and evaluated for applicability to inertial navigation system (INS) correction in global navigation satellite system-denied space.FindingsThe performance analysis of existing munition shows that small and relatively inexpensive micro-electro-mechanical system-type inertial sensors are required. A review of the parameters of existing devices of this type has shown that they are subject to measurement errors that do not allow them to achieve the delivery accuracy expected of precision missiles. The most promising navigation correction methods for manoeuvring flying objects have been identified.Originality/valueThe information presented in this paper is the result of the first phase of a project and presents the results of the requirements selection, initial sizing and preliminary design of the navigation system. This paper combines a review of the current state of the art in missile systems and an analysis of INS accuracy including the selection of sensor parameters.

This paper aims to study the design of deep learning adaptive algorithms for different types of inertial navigation systems. Underwater inertial navigation plays a crucial role in fields such as ocean exploration, but improving its accuracy faces challenges, and combined navigation methods are often adopted. Deep learning is widely applied in inertial navigation, which can be used for sensor data processing, positioning, attitude estimation, and can also be combined with other navigation technologies. Based on previous research, this paper delves into the characteristics of different inertial navigation devices, combines with large language models, and proposes an LLM-based adaptive parameter selection expert system. This system can select appropriate deep learning network parameters based on parameters such as the material, accuracy, and service life of the inertial navigation device to ensure the matching between the model and the inertial navigation device. The network design is divided into a displacement prediction module and a heading angle prediction module, and different targeted designs are adopted. The experiment was conducted in the Yellow Sea, equipped with strapdown fiber optic inertial navigation systems and strapdown laser inertial navigation systems, and targeted training was carried out according to the characteristics of the two sets of systems. The fiber optic inertial navigation system has an earlier factory time and a large data scale; the laser inertial navigation system has a recent factory time and high accuracy. The experimental results show that the error is significantly suppressed after adopting the deep learning algorithm, and it is feasible to use the expert system to drive the setting of targeted parameters, which has the value of promotion and research.

With the rapid development of computer technology and communication technology, pedestrian navigation system(PNS) began to enter the real practical stage. PNS is a method of track reckoning based on strapdown inertial navigation system. However, inertial navigation system is prone to error accumulation with time, resulting in low positioning accuracy and practicability. Low cost inertial measurement unit(IMU) device has low accuracy, and the inertial navigation system has accumulated error, and the system is easy to diverge. Apart from those above, civil GPS accuracy is not stable, and poor GPS signal in a multipath environment may lead to errors, and GPS signals can not be used indoors. It is necessary to design a kind of navigation method to adapt low cost IMU. In this paper, it is presented that a foot inertial navigation system which is based on low cost MEMS inertial sensors is presented. According to the characteristics of pedestrian zero-velocity during walking steady state, an inertial navigation method based on Zero Velocity Update (ZUPT) is adopted. This paper uses air pressure, GPS to assist ZUPT method to correcting navigation parameters through Kalman filter. Based on the implementation scheme of pedestrian navigation system proposed in this paper, the steady state of walking were experimentally verified through the actual test data. The experimental results show that the proposed method can provide efficient, reliable and continuous position and navigation services.

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) are very practical method to get high precision navigation. GPS/INS data Fusion in navigation has been a research topic for decades. Usually the update time of GPS an INS is different. The update time of GPS is 20HZ, and the time of INS is 100HZ or higher. Since GPS and INS are mainly methods in navigation system. Techniques of fusing GPS and INS data are in high demand in navigation industry. Usually the Karman filter is used to fuse the data from GPS and INS. But the Kalman filter is model-dependent and a priori the model parameters need to be known. In this work, a generic and intelligent approach of GPS and INS data fuse using fuzzy interpolation before Kalman algorithm is proposed. First a simplified interpolation model is introduced in a ordinary GPS and INS data fusion. Second a fuzzy rule-based system is established to fuzzy interpolation with objective fuzzy method. Then the parameters of objective fuzzy system are identified, the best n-rule is determinate and the optimal rule base system is found. Finally this approach is applied on an example for a car GPS and INS data fusion, and the results are calculated with computer system to demonstrate the advantages of this approach.

Global Positioning System and Inertial Navigation System can be used to determine position and velocity. A Global Positioning System module is able to accurately determine position without sensor drift, but its usage is limited in heavily urbanized environments and heavy vegetation. While high-cost tactical-grade Inertial Navigation System can determine position accurately, low-cost micro-electro-mechanical system Inertial Navigation System sensors are plagued by significant errors. Global Positioning System is coupled with Inertial Navigation System to correct the errors, while Inertial Navigation System itself can be used to provide navigation solution during a Global Positioning System outage. Data from Global Positioning System and Inertial Navigation System can be integrated by extensive Kalman filtering, using loosely coupled integration architecture to provide navigation solutions. In this study, real-time low-cost loosely coupled micro-electro-mechanical system Inertial Navigation System/Global Positioning System sensors have been used for pedestrian navigation. Trial runs of Global Positioning System outages have been conducted to determine the accuracy of the system described. The micro-electro-mechanical system Inertial Navigation System/Global Positioning System can successfully project a trajectory during a Global Positioning System outage and produces a root mean square error of 9.35 m in latitude direction and 10.8 m in longitude direction. This technology is very suitable for visually impaired pedestrians.

Provision of solution of the problem of autonomous navigation on a long time interval is considered. The main problem when using inertial navigation systems is caused by the accumulation of positioning and angular orientation errors. It is shown that this problem can be solved by combining methods of nonlinear filtering in processing the results of inertial measurements of platform-free inertial navigation systems and methods of reducing the dimensionality of the vector of navigation parameters on trajectory sections described by analytical models. Orthodromic (shortest) and circular intervals, most typical for program trajectories of transport objects – highways, railroads, airlines, etc., are considered as such trajectory sections. For circular intervals, analytical dependencies of the spatial coordinates of strapdown inertial navigation systems were obtained for the first time. These dependencies make it possible to reduce the dimension of the vector of navigation parameters and, as a consequence, to build a model of an autonomous observer of navigation parameters to use equations excluded from the general system of these parameters. It is shown that using stochastic nonlinear filtering methods it is possible to solve the problem of noise-resistant autonomous navigation on trajectory intervals described by analytical models. The proposed approach to solving the problem of autonomous navigation of moving objects makes it possible to significantly reduce computational costs in the practical implementation of algorithms for nonlinear filtering of current navigation parameters in comparison with traditional models of strapdown inertial navigation systems. The effectiveness of the proposed approach is illustrated with a numerical example. The results obtained are useful in developing navigation support for various transport objects moving along program trajectories, for example, aircraft for various purposes, railway, road, river transport, etc.

The necessity of integrated navigation complexes (INC) construction is substantiated. It is proposed to include in the complex the following inertial systems: inertial, satellite and visual. It helps to increase the accuracy of determining the coordinates of unmanned aerial vehicles. It is shown that in unfavorable cases, namely the suppression of external noise of the satellite navigation system, an increase in the errors of the inertial navigation system (INS), including through the use of accelerometers and gyroscopes manufactured using MEMS technology, the presence of bad weather conditions, which complicates the work of the visual navigation system. In order to ensure the operation of the navigation complex, it is necessary to ensure the suppression of interference (noise). To improve the accuracy of the INS, which is part of the INC, it is proposed to use the procedure for extracting noise from the raw signal of the INS, its prediction using neural networks and its suppression. To solve this problem, two approaches are proposed, the first of which is based on the use of a multi-row GMDH algorithm and single-layer networks with sigm_piecewise neurons, and the second is on the use of hybrid recurrent neural networks, when neural networks were used, which included long-term and short-term memory (LSTM) and Gated Recurrent Units (GRU). Various types of noise, that are inherent in video images in visual navigation systems are considered: Gaussian noise, salt and pepper noise, Poisson noise, fractional noise, blind noise. Particular attention is paid to blind noise. To improve the accuracy of the visual navigation system, it is proposed to use hybrid convolutional neural networks.

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

Providing high accuracy of the coordinates and trajectories of objects by measurements conducted in navigation systems and complexes is an urgent task, which improves safety and efficiency of different modes of transport. However difficult environmental conditions, where vehicles are commonly used, stipulate influence of different factors on performance of onboard satellite navigation receivers, which are used as basic navigation devices for ground vehicle nowadays. Setting on cars used for common purposes additional navigation devices, which provide better performance, in most cases is economically unreasonable. Economically reasonable ways to improve onboard navigation complexes of vehicles, which are used for common purposes, are examined in this article. Functional diagram and principles of work of navigational complex, which uses the satellite navigation receiver and simplified variant of inertial navigation system is pointed as well. Also, the justification of methods for minimizing the error formats of coordinates and trajectories of moving objects based on information processing in multipositional, in particular satellite-inertial navigation systems and complexes, is presented. The obtained research results give an opportunity to develop an algorithm for coordinate refinement, which can be implemented in the improved on-board navigational complex of vehicle. KEY WORDS: NAVIGATION SYSTEMS AND COMPLEXES, INERTIAL SENSORS, NAVIGATION DEFINITIONS, ACCURACY AND RELIABILITY OF COORDINATES AND TRAJECTORIES OF MOVING OBJECTS, ELLIPS OF ERRORS, PROBABILISTIC-GEOMETRIC METHODS.

The traditional inertial navigation system-based indoor pedestrian navigation method is not suitable to all the conditions, since its data fusion model is designed only for a single condition. In order to achieve better performance, a two-mode navigation method for indoor pedestrian navigation is proposed in this work, which includes the stance mode and the swing mode. When the person’s foot is in a stance phase, the stance mode is used to correct the velocity error and yaw error. When the person’s foot is in a swing phase, the swing mode works and only the yaw error is able to be corrected. For verification, a real indoor test has been done to assess the performance of the proposed two-mode method. The position root-mean-square error (RMSE) value is 1.9265 m during a 176-m traverse, and the RMSE of yaw is 0.20734 rad. These results demonstrate that our method is effective to reduce the error compared with the conventional schemes.

Beidou satellite navigation system (BDS) signal is blocked during dynamic positioning which can lead to poor geometry structure of visible satellites and serious multipath effect. In this contribution, the advantages of inertial navigation system (INS), autonomy and short-term accuracy, are used to speed up the BDS ambiguity resolution (AR). However, the accuracy of general INS cannot meet the fixed BDS original carrier ambiguity requirement. BDS can broadcast three frequency observations, so the virtual observations of long wavelength and short wavelength are formed by linear combination, which make the INS precision meet the fixed ambiguity requirement and achieve high precision positioning, respectively. The short wavelength observations ambiguity due to the relatively large noise is difficult to reliably fixed. To solve this problem, Empirical mode decomposition (EMD) can be used to decompose the error of the short wavelength observations into two parts: systematic error and random error. Systematic error is used to correct the short wavelength ambiguity float solution and reduce the ambiguity search space, to achieve a reliable fixed purpose, finally solving the baseline solution. The experimental results show that the algorithm is more fast and reliable than the traditional algorithms, and more suitable for dynamic positioning in the complex environment.

With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) is a very practical method to get high-precision measurement data. Usually, the Kalman filter is used to fuse the data from GPS and INS. In this paper, a robust method is used to measure vehicle sideslip angle and yaw rate, which are two important parameters for vehicle stability. First, a four-wheel vehicle dynamic model is introduced, based on sideslip angle and yaw rate. Second, a double level Kalman filter is established to fuse the data from Global Positioning System and Inertial Navigation System. Then, this method is simulated on a sample vehicle, using Carsim software to test the sideslip angle and yaw rate. Finally, a real experiment is made to verify the advantage of this approach. The experimental results showed the merits of this method of measurement and estimation, and the approach can meet the design requirements of the vehicle stability controller.

Navigation systems for ships mainly include inertial navigation system (INS), “BeiDou” satellite navigation system, Global Positioning System (GPS), and Doppler navigation system and so on. The characteristics of each system are different. In order to improve the accuracy and fault-tolerant ability of naval-ship navigation system, an integrated navigation algorithm based on Measurement-based adaptive Kalman filtering algorithm (MAKF), improved Suge-Husa algorithm and non-reset federated Kalman filter structure is proposed in this paper. The algorithm uses the short-term high precision of the inertial navigation system and the prior velocity information of the ship to adaptively estimate the measurement noise characteristics of the satellite navigation system. When the satellite navigation system is unable to locate, the INS and the log are combined with the help of the prior velocity information. The simulation results show that the algorithm can effectively exploit the characteristics of each navigation system, and can output high-precision navigation results even when the GPS/BeiDou satellite navigation system fails.

Submarines in the underwater sailing need a safe, reliable, high accurate, and covert well navigation system. Inertial navigation system (INS) is the core of underwater navigation. But the inertial navigation system gathers information based gyroscope, accelerometer and other sensors. In accordance to Newton's laws of mechanics, their own speed, location and other information is calculated by integral recursion. Since the recursive work of INS, positioning error gradually increases with time elapsing. Gravity and gravity gradient aided navigation as a passive autonomous navigation are more and more focused on, Selection of the gravity gradient matching area is one of the key to gravity gradient matching navigation. Earth's marine area is enormous, underwater environment is complex. Take advantage of multi-feature information fusion of gravity gradient full tensor, one hand a wider range of matching area can be got, to gain wider path planning area. on the other hand, the positioning accuracy of assisted navigation system can be inproved.