Multi-Sensor Integrated Navigation System for Ships Based on Adaptive Kalman Filter

The sidewalk is often located in complex environments such as buildings or covered by dense trees. Satellite navigation systems are susceptible to the surrounding environment, resulting in multipath effects, resulting in reduced positioning accuracy. Therefore, the global satellite navigation system (GNSS) and inertial navigation system (INS) based on kalman filter (KF) algorithm are studied, and the navigation algorithm and system model suitable for pedestrians are given. Considering that the pedestrian speed is low, the satellite navigation system obtains the carrier orientation information (heading angle), which also produces a large error. Therefore, the pedestrian dead reckoning (PDR) correction method is adopted to make the positional accuracy of the integrated navigation continuously stable. This paper proposes a pedestrian navigation system solution based on a single navigation device.

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.

How to achieve continuous, reliable and accurate positioning performance using low-cost sensors is one of the main challenges for aviation navigation system. Global Positioning System (GPS) can provide the primary means of navigation in a number of aviation navigation applications (e.g., manned and unmanned aircraft vehicle, airport ground vehicle). However, GPS signal deteriorations typically occur due to aircraft itself during maneuvering, ionospheric scintillation, Doppler shift, multipath and so on. Thus, there is a need to research GPS augmentation strategies which can be used in the Communication, Navigation, Surveillance/Air Traffic Management. GPS integration with Inertial Navigation system (INS) is one of the key strategies. But once GPS signal outages, the integrated navigation system works in pure INS, and positioning accuracy deteriorates with time. When using low cost GPS/INS integration, a primary problem is the rapid performance deteriorate during GPS outages. To provide continuous, accurate and reliable positioning information in aviation, discrete grey prediction model (DGPM) aided fusion methodology is proposed. The DGPM provides pseudo-GPS position information for INS during GPS outages. The mathematical model of integrated navigation system is established, including INS error equations, Kalman filter and DGPM. The model works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road test is performed. The comparison results show that accuracy of longitude and latitude are improved by more than 80% and 70%, respectively. The DGPM can effectively provide position corrections for standalone INS during GPS outages.

Navigation signals are the basis of satellite navigation systems, which determine the navigation systems' performance (such as tracking accuracy, anti-interference capability etc.), and are determined by some constraints (such as radio frequency resources, construction cost, technology level etc.). The aim of signal design is to select "proper" signals considering these above factors and make tradeoffs between performance and constraints. This paper will summarize the signal structure evolution of present satellite navigation and conclude some principles and issues which will be greatly valuable to our 2nd satellite navigation system. There's four main satellite positioning systems presently, the U.S. Global Positioning System (GPS), the Russian Global Navigation Satellite System (GLONASS), the Europe Galileo satellite navigation system and the Chinese BD-1 satellite positioning system. Among these systems, the signal characteristics of GPS and Galileo are good reference to our 2nd satellite navigation system. The improvement of GPS signals is a main aspect of GPS modernization. The aim of GPS modernization is mainly to modernize its signal structure so as to improve the whole systems' performance. The signal structure of Galileo has been revised for several times, and its present signal is the results of long time signal design. From GPS modernization and Galileo signals status, we can summarize some key specifications of navigation signals and some technologies to support these specifications. Based on this, we'll discuss some problems which should be attentive in our country's 2nd satellite navigation system and give some suggestions. This paper is organized into five sections. The first section will give a brief introduction of present satellite navigation system and their main features. Section two will review the GPS and Galileo signals structure evolution, including GPS modernization, Galileo signals status and the comparison of two systems' signals. In the third section, the relationship between signal parameters and system performance will be analyzed, including RF signal characteristics, modulation type, ranging code (such as code type, length and rate) and navigation data. In section four we will discuss some inspiration and technologies concerning our 2nd satellite navigation system, such as the use of multiple frequency signals (ionospheric delay estimation and carrier ambiguity resolution), pilot tones & dataless channel, binary offset carrier (BOC) modulation, and so on. The last section is conclusion, in which the whole paper will be retrospected and some useful conclusion will be given.

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.

Satellite navigation systems, inertial navigation systems and integrated systems (satellite navigation systems and inertial navigation systems) are used to determine navigation parameters. But satellite navigation systems are not autonomous. Although inertial navigation systems are autonomous, they accumulate errors over time that require external correction. A new method of autonomous determination of latitude and longitude of moving objects is proposed. Unlike standard methods, this method does not use double integration of accelerometer signals. To determine latitude and longitude, gyroscope signals are used, which are part of the inertial measurement unit (IMU). To implement the method of autonomous determination of latitude, it is enough to have the signals of the IMU gyroscopes, the elements of the direction cosines matrix, the angles of rotation of the object, and their derivatives. For the autonomous determination of longitude, it is necessary, in addition, to know the initial value of the longitude. The use of the Luenberger observer to determine of latitude and longitude is considered.

With the continuous development of positioning technology in today’s world, the accuracy requirements for navigation and positioning are also getting higher and higher. Global Positioning and Navigation System (GPS) can provide high-precision long-term navigation and positioning information. However, it has a strong dependence on the external environment, which means that it is easily disturbed by environmental changes and affects the accuracy of navigation and positioning and even leads to positioning failure. The inertial navigation system (INS) is an autonomous navigation system. It uses sensors to measure the specific force and angular velocity of the carrier for positioning and navigation, which means that it is less affected by the environment. However, the inertial navigation device will produce a certain initial error due to the restriction of the manufacturing level, and the error will increase with time, so the inertial navigation method is not suitable for long-term navigation. Therefore, it is of great practical significance to realize satellite/inertial navigation integrated navigation by combining the respective advantages of satellite navigation and inertial navigation methods and avoiding their respective disadvantages. This paper is aimed at studying the satellite/inertial navigation integrated navigation method based on the improved Kalman filter algorithm. The satellite inertial navigation integrated navigation experiment is carried out based on the improved Kalman filter algorithm. In the experiment, the noise reduction experiment of the designed satellite inertial navigation system was carried out by using the filtering noise reduction function of the improved Kalman filter algorithm, and the conclusion was drawn after the experiment. The navigation accuracy of the satellite inertial navigation system is improved by a total of 2 m after the improved Kalman filter algorithm is used to filter the noise reduction.

With the development of the intelligent vehicle navigation systems and vehicle networking systems, vehicle positioning and state estimation become very important. This paper investigates the integration of Global Positioning System (GPS), Inertial Navigation System (INS) and Dead Reckoning based on Anti-lock Braking System (ABSDR) positioning based on Kalman filter, and aims at achieving full-time vehicle positioning. In this paper, we use Kalman Filter to estimate INS positioning error based on GPS or ABSDR solution, take the INS solution compensated with estimated error as system output. This paper introduces INS positioning algorithm, dead reckoning method based on the ABS wheel speed and Kalman filtering method. After that, simulation analysis and experimental verification of the positioning algorithm are carried out. The results show that the system output high-precision high-frequency positioning information when GPS is available, and when GPS is unavailable, INS positioning errors can be compensated well with ABSDR introduced, achieving the full-time positioning of the vehicle.

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.

When the vehicle passes through the tunnel or bridge, Global Positioning System (GPS) will fail due to signal occlusion or interference. In order to improve the positioning accuracy of vehicle-carried inertial navigation system (INS) in GPS rejection environment, the lateral constraint method based on vehicle-carried INS is studied in this article. According to the characteristics of lateral constraint, in the area with good GPS signal, installation errors between the INS body coordinate frame and the vehicle coordinate frame are estimated at the same time of integrated navigation. In the GPS-denied area, the estimated installation errors are compensated to calculate the reference velocity at first. After compensation, the difference between INS velocity and reference velocity is taken as the measurement. Finally, the Kalman filter model is established to correct the inertial navigation position. In addition, the simulation and experimental verification of the proposed lateral constraint method are carried out. The results show that the INS positioning accuracy can be improved above 50% by using the lateral constraint method in GPS-denied environment, which verifies the effectiveness of the proposed lateral constraint method.

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 parameters such as vehicle heading, linear speed and latitude and longitude are determined today, as a rule,using satellite navigation systems. However, there is a need for autonomous determination of navigation parameterswhen satellite navigation system signals are unavailable for one reason or another. For example, satellite navigationsystem signals can be jammed by electromagnetic countermeasures.Autonomous systems for determining the vehicle navigation parameters are inertial navigation systems, the basis ofwhich is the double integration of signals from accelerometers, as well as the integration of signals from gyroscopes toform one or another coordinate system, if we are talking about strapdown inertial navigation systems (SINS).Integration of the output signals of accelerometers and gyroscopes, which contain errors, leads to the accumulation ofSINS errors. To increase accuracy, SINS are integrated, for example, with satellite navigation systems. However, suchsystems cease to be completely autonomous.The article uses the method of autonomous determination of vehicle latitude and longitude, which is a new method ininertial navigation. The method is based on the strapdown inertial technology, which, unlike the standard methods usedin SINS, does not use the integration of the output signals of accelerometers and gyroscopes. To determine the angles oflatitude and longitude, the signals of the gyroscopes included in the inertial measurement unite (IMU) are used.As a result of a semi-natural experiment, the efficiency of the method of autonomous determination of latitude andlongitude was verified. Flight data of a small-sized aircraft were used for experimental verification. The data containedinformation about the angles of the heading, pitch, roll of the aircraft, the output signals of three micromechanicalgyroscopes, three micromechanical accelerometers, information about latitude, longitude and altitude measured by thereceiver of the satellite navigation system, as well as information about the horizontal and vertical components of speedand current time. Plots of calculated values of latitude and longitude and values measured using the receiver of thesatellite navigation system are given. From the given plots and tables, it can be seen that from a certain time, thecalculated values coincide with the "reference" ones, which confirms the efficiency of the method of autonomousdetermination of latitude and longitude.

In the last decade, the demand for accurate land-vehicle navigation (LVN) in several applications has grown rapidly. In this context, the idea of integrating multisensor navigation systems was implemented. For LVN, the most efficient multisensor configuration is the system integrating an inertial navigation system (INS) and a global positioning system (GPS), where the GPS is used for providing position and velocity and the INS for providing orientation. The optimal estimation of the system errors is performed through a Kalman filter (KF). Unfortunately, a major problem occurs in all INS/GPS LVN applications that is caused by the frequent GPS signal blockages. In these cases, navigation is provided by the INS until satellite signals are reacquired. During such periods, navigation errors increase rapidly with time due to the time-dependent INS error behavior. For accurate positioning in these cases, some approaches, known as bridging algorithms, should be used to estimate improved navigation information. In...

This paper describes the theoretical and practical stages through development to testing of an integrated navigation system, specifically composed of an Inertial Navigation System (INS), and Global Positioning System (GPS). Among various INS/GPS integration strategies, our aim is to construct a mathematical model of INS and GPS systems. A Kalman filter is designed and implemented depending upon these models. Besides these, the performance of the developed system is evaluated with real data recorded by the sensors. A comparison with a reference system and also with a loosely coupled system is done to show the Superiority of the tightly coupled structure. The strap down inertial navigation system (SINS) uses (ENU) frame and the integrated system uses position and velocity as measurements. The system model of the integrated system for Kalman filtering are derived and modeled as 27-states. These states estimation system showed clearly the application of fundamental modeling and filtering techniques. The simulation is built on the integrated system INS/GPS/GNSS and the trajectory generator data for flight simulation to be used as a base line algorithm contributing for design, analysis and develop a guidance and control strategy which compensates for the effects of the uncertainties accompanying the navigation systems produced by INS alignment.

Land vehicle positioning relies mostly on satellite navigation systems such as the Global Positioning System (GPS). However, GPS signals may be degraded or suffer from blockage in urban canyons and tunnels, and the positioning information provided is interrupted. One solution for such a problem is to integrate GPS with an inertial measurement unit (IMU) and the navigation solution is achieved using an estimation technique which is traditionally based on a Kalman filter (KF). In order to have a low cost navigation solution for land vehicles, MEMS-based inertial sensors are used. To further reduce the cost a reduced inertial sensor system (RISS) which consists of only one gyroscope and a speed sensor is integrated with GPS. The position and velocity errors can be estimated by a KF relying on RISS dynamic error model and GPS position and velocity updates. However, low-cost MEMS sensors suffer from complex error characteristics, which are difficult to model by the linearized KF models. The positional accuracy of the integrated system can be improved using Parallel Cascade Identification (PCI) that is cascaded with the KF. The proposed augmented KF-PCI method can handle both linear and nonlinear system errors as the linear parts of the errors are modeled inside the KF and the nonlinear residual RISS errors are modeled by PCI. The performance of this method is examined by road test trajectories in a land vehicle and compared to KF.