Inertial navigation system positioning assisted by star sensor

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.

A Star Sensor based Fiber Optical Gyroscope Error Separation method (SSFES) was proposed, which can be applied in the Fiber Optical Gyroscope (FOG) performance evaluation and fault diagnosis for the space-borne Inertial Navigation System (INS) & Celestial Navigation System (CNS) combined navigation system. The method takes the satellite rotational angular rate and the attitude quaternion to separate the long-term drift component from the FOG output and provides the useful information for FOG error compensation and model construction. The output quaternion of the star sensor was utilized to reconstruct the satellite rotational angular rate information, which was then used to separate the error component from the FOG output. Simulations have been carried out to verify the method and have shown that the error component of FOG output can be extracted efficiently by SSFES and the algorithm is easy to be performed by the onboard computers.

The major challenge of current strapdown inertial navigation system/celestial navigation system (SINS/RCNS) is failing to calibrate all the error coefficients of gyro and star sensor in-flight accurately, which will lead to the attitude accuracy degradation. To address this question, this paper proposes an optimal calibration method based on observability analysis. Here, we derive the star sensor measurement model with respect to initial alignment errors, gyro errors, and star sensor installation errors. And the observability analysis is employed to explain the reason why all the error coefficients can be estimated effectively by three maneuvers merely. Finally, the optimal observation sequence is determined based on the fisher information matrix (FIM). The proposed optimal calibration method is evaluated by a representative suborbital flight vehicle trajectory, which represents significant improvements in estimation accuracy of error coefficients, and realizes the attitude accuracy enhancements.

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.

Attitude control system plays the important role for to maintain the satellite to desired orientation. To control the satellite it is necessary to do the attitude stabilization. Attitude stabilization achieved by Star sensor, sun sensor, Earth sensors. Attitude control is mainly use for antenna pointing accuracy, camera focus to earth surface and solar panel pointing toward sun. Due to tumbling effects, satellite will rotate all the direction in the space. To maintain the orientation of the satellite it is necessary to design the attitude determination and control. Satellite consider as the rigid body. Inertia matrix describes the rigid body dynamics. The orientation of the satellite determine by Euler angle and Quaternion. The Low earth orbiting satellite will have enormous amount of aerodynamic drag stinking the satellite body and gravitational attraction another problem. Because of that satellite dwell, time is reduced. It means satellite more time spending particular part of the earth. The attitude estimation is measures by the orientation of vectors. Attitude estimation means to find the position and orientation of flying object with respect to the fixed reference of reference. Vector remains considered in the frame of reference to compute for find the orientation of the body of the satellite in the inertial reference system. The Earth is an inertial reference frame, and Satellite is a body frame. Attitude sensor used to measure the satellite orientation in the reference frame. This will help in accurately predicting the orbit deviation and a control system to correct if any by providing the satellite momentum means ‘mass in motion’ changes in a body rapidly in Low earth orbit due to centripetal force acting on a satellite. Attitude control system (ACS) need the numerical simulation to find the required torque demand by the help of difference between reference input (Attitude) signal and feedback signal measure by attitude sensor to trim the control surface maintain the actuator required orientation . The results will consist of two parts the first part consisting of the attitude estimation using Euler angle and Quaternion method, second part consist of estimate the control torque from magnetic torquer and error estimation using non-linear filter (Unscented Kalman Filter) with MATLAB simulation. Â

State estimation techniques are important tools for analyzing systems that contain states that are not directly measureable. If the estimated states are used, for example, in place of the true states in a feedback controller, the accuracy and stability of the estimates becomes crucial for the safe and effective execution of the controller. This is especially important in aircraft control applications, where safety is an essential concern. Because of this, the stability characteristics of the state estimation are investigated. Additionally, two different nonlinear Kalman filters are considered and compared with respect to various design parameters. This work considers the sensitivity and stability characteristics of nonlinear state estimation through the aircraft attitude estimation problem. This problem is approached using sensor information from Global Positioning System (GPS) and Inertial Navigation System (INS) in order to obtain estimates of the aircraft attitude angles. This case study uses experimentally collected flight data from subscale aircraft to derive estimation results. The goal of this work is to obtain a better understanding of the properties of nonlinear Kalman filters in order to make more informed decisions regarding the selection and tuning of these filters for different real-world applications.

A new method for dynamic measurement of deflections of the vertical (DOV) is proposed in this paper. The integration of an inertial navigation system (INS) and global navigation satellite system (GNSS) is constructed to measure the body's attitude with respect to the astronomical coordinates. Simultaneously, the attitude with respect to the geodetic coordinates is initially measured by a star sensor under quasi-static condition and then maintained by the laser gyroscope unit (LGU), which is composed of three gyroscopes in the INS, when the vehicle travels along survey lines. Deflections of the vertical are calculated by using the difference between the attitudes with respect to the geodetic coordinates and astronomical coordinates. Moreover, an algorithm for removing the trend error of the vertical deflections is developed with the aid of Earth Gravitational Model 2008 (EGM2008). In comparison with traditional methods, the new method required less accurate GNSS, because the dynamic acceleration calculation is avoided. The errors of inertial sensors are well resolved in the INS/GNSS integration, which is implemented by a Rauch–Tung–Striebel (RTS) smoother. In addition, a single-axis indexed INS is adopted to improve the observability of the system errors and to restrain the inertial sensor errors. The proposed method is validated by Monte Carlo simulations. The results show that deflections of the vertical can achieve a precision of better than 1″ for a single survey line. The proposed method can be applied to a gravimetry system based on a ground vehicle or ship with a speed lower than 25 m/s.

Characterization of Cubature Kalman Filter for GPS Delayed Environments in INS-GPS Integrated Navigation

The starlight navigation system needs the horizontal attitude information provided by the inertial navigation system as its horizontal reference. When the zero position of the accelerometer changes in the undammed inertial navigation system in the critical condition, it is easy to cause the error oscillation and divergence, which directly affects the accuracy of the horizontal reference information of the auxiliary star sensor. Therefore, under the condition of ship-borne use, consider the combination of strapdown inertial navigation system, starlight navigation system and electronic log. The Kalman filter with velocity damping compensates the dynamic error of inertial navigation. The calibration and readjustment technology of the ship borne starlight/inertial combined system is based on the horizontal velocity damping, and the position error and misalignment angle are completed with the help of accurate reference information of starlight navigation.

We describe an integrated navigation system based on Global Navigation Satellite Systems (GNSS), an Inertial Navigation System (INS) and terrestrial ranging technologies that can support accurate and seamless indoor-outdoor positioning. To overcome severe multipath disturbance in indoor environments, Locata technology is used in this navigation system. Such a “Locata-augmented” navigation system can operate in different positioning modes in both indoor and outdoor environments. In environments where GNSS is unavailable, e.g. indoors, the proposed system is designed to operate in the Locata/INS “loosely-integrated” mode. On the other hand, in outdoor environments, all GNSS, Locata and INS measurements are available, and all useful information can be fused via a decentralised Federated Kalman filter. To evaluate the proposed system for seamless indoor-outdoor positioning, an indoor-outdoor test was conducted at a metal-clad warehouse. The test results confirmed that the proposed navigation system can provide continuous and reliable position and attitude solutions, with the positioning accuracy being better than five centimetres.

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.

Although a USV navigation system is an important application of unmanned systems, combining Inertial Navigation System (INS) with Global Positioning System (GPS) can provide reliable and continuous solutions of positioning and navigation based on its several advantages; the random error characteristics of INS and the instability derived from the GPS signal blockage represent a potential threat to the INS/GPS integration of USV. Under this background, a composition framework based on nonlinear generalization capability of support vector machines (SVM) and multi-resolution ability of wavelet transform is used to solve the difficulty that the INS suffers from the interference of stochastic errors, and the dynamic information of the USV is not influenced. An innovative fuzzy broad learning structure based on the broad learning (BL) method is utilized in the INS/GPS integration of USV, in which the navigation information of INS and GPS are deemed as the input of the Fuzzy Broad Learning System (FBLS) to train the network, and then the trained network of FBLS and navigation information of INS are applied for estimating the optimal navigation solution during the GPS signal blockage. Based on the USV platform, a sea trial was carried out to confirm the validity and feasibility of the proposed method by comparing with existing algorithms for INS/GPS integration. The experimental results show that the proposed approach could achieve the better denoising effect from random errors of INS and provide high-accuracy navigation solutions during GPS signal blockage.

The high precision positioning and navigation of underwater vehicles is one of the key technologies for deep-sea exploration and ocean exploration, the underwater localization systems can be categorized into two main types: Inertial Navigation System (INS) and acoustic positioning system. INS is widely used in underwater navigation on account of its autonomy, however, without the information aiding from additional sensors, the errors of inertial measurement units accumulate with time passes. The acoustic positioning system can be divided into three types: Long Base Line (LBL), Short Base Line (SBL) and Ultra Short Base Line (USBL). They are defined according to the distance between transponders. The short baseline (SBL) positioning system includes more than 3 transponders to form an acoustic array, and the formation of the array is normally triangle or quadrilateral. The distance between the transponder is accurately measured and generally more than 10m. The acoustic array coordinate system is built based on the transponder and its relationship with the carrier coordinate system of the vehicle is determined by conventional method, the main disadvantage of the SBL positioning system is that the baseline length is relatively large in order to achieve high accuracy in deep-water measurement. However, as the lateral length of the vehicles is small, it is difficult to make sure the transponders be placed way from each other, meanwhile it is difficult to calibrate multiple transponders and avoid noise. To solve this problem, this paper present an inertial navigation system and single range navigation system (SRNS) based on one acoustic transponder, the navigation system consist of an acoustic transponder, a pressure transponder and inertial sensors mounted on the vehicle and an answering device transponder which is laoid out at tens of meters deep straight under the buoy. This paper studies the measurement equation's nonlinearity effect of the INS/SRNS, establishes state equation and measurement equation of the INS/SRNS integrated system. To deal with the nonlinear noise problems of transponders, meanwhile, in view that fault detection based on traditional state chi-square hypothesis testing cannot determine the specific cause of the fault, and could only determine the validity of measure information, a new fault detection algorithm based on dual-state method is adopted. The application of this method on underwater integrated navigation system demonstrates that the algorithm can rapidly and accurately detecting and identify the faults in the system. Experiments on QIANDAO Lake show that the proposed method restrains the divergence trend error of the integrated system's navigation, which verify the effectiveness of the single range integrated navigation.

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.

Performance evaluation of Cubature Kalman filter in a GPS/IMU tightly-coupled navigation system