Low-cost Strapdown Inertial Navigation System Research Articles

GNSS-assisted optimal alignment method for low-cost SINS motion of vehicle

Abstract To improve the alignment accuracy and environmental applicability of the micro-electromechanical system (MEMS)-based strapdown inertial navigation system (SINS), this study proposes a global navigation satellite system (GNSS)-assisted multi-vector determination of the attitude optimal indirect coarse alignment. Using the inertial information output from the inertial measurement unit and the velocity information from the GNSS, a simplified velocity observation vector is constructed and the velocity lever arm stemming from the mounting position inconsistencies of the GNSS and SINS is fed back into the velocity of the carrier system. When constructing the observation vectors, the integration interval is shortened by reconstructing the two-vector integration formula for reducing the cumulative error of the inertial device. The attitude matrix problem is defined as the Wahba problem, which is solved using the singular value decomposition method. Based on the relationship between gyro zero bias and misalignment angle, the corresponding state and measurement equations are designed. Furthermore, owing to the measurement noise uncertainty, the adaptive traceless Kalman filtering algorithm is introduced to realize the effective adaptation processing of the measurement noise. More accurate attitude matrix estimates are obtained by continuously correcting the carrier system transformation matrix. The running car experiment results show that the proposed method exhibits higher alignment accuracy and environmental applicability than the current MEMS strapdown inertial navigation coarse alignment method and the traditional optimization-based alignment method.

An Improved In-flight Alignment Method Based on Backtracking Navigation for GPS-aided Low Cost SINS With Short Endurance

Aiming at the problem that the low-cost Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) cannot effectively achieve fast in-flight alignment with short-endurance, an improved alignment method based on backtracking navigation is proposed. Firstly, the existing backtracking alignment scheme is refined by fully considering the low-cost inertial sensor biases and correcting the correlated errors, which can effectively suppress the sensor biases that cannot be ignored while making full use of the limited observation information. Secondly, a cubature Kalman filter (CKF) based on Lie group is designed to correct the sensor biases. The corresponding errors encountered during the construction of the observation vectors for alignment are directly compensated by filtering, thus avoiding the traditional schema of coarse alignment followed by fine alignment, and simplifying the alignment process. The effectiveness of the proposed in-flight alignment method is demonstrated via ground vehicle tests. The test results show that, compared with the existing advanced alignment methods, the proposed method has higher alignment accuracy, faster alignment speed and better stability, and is applicable to low-cost SINS for short-endurance alignment.

A New Kalman Filter-Based In-Motion Initial Alignment Method for DVL-Aided Low-Cost SINS

The inertial measurement unit (IMU) biases, DVL lever arm and installation misalignment angles between IMU and doppler velocity log (DVL) have a great influence on in-motion initial alignment for DVL-aided low-cost strap-down inertial navigation system (SINS). A new Kalman filter-based initial alignment method is proposed in this paper. To weaken the effects of IMU biases, DVL lever arm and installation misalignment angles between IMU and DVL, a closed-loop scheme is presented to simultaneous estimate and compensate these parameter errors and the body attitude matrix based on a linear state-space model, which improves the accuracy of vector observations. The constant matrix from initial body frame to initial navigation frame can be determined based on the vector observations by Davenport's q-method. Simulation results illustrate that, for the DVL-aided low-cost SINS, the alignment performance of the proposed initial alignment method is better than that of the compared initial alignment methods.

A Hybrid Data Fusion Approach to AI-Assisted Indirect Centralized Integrated SINS/GNSS Navigation System During GNSS Outage

The main challenges for integration between low-cost strap-down inertial navigation system (SINS) and global navigation satellite system (GNSS) are to manage the non-Gaussian measurement noises and to access the reliable measurements during GNSS outages. Therefore, in this paper, some approaches are taken in the proposed integrated navigation system: (1) using an iterative empirical mode decomposition (EMD) interval thresholding de-noising method for low-cost inertial measurements to provide smoother and more accurate SINS measurements with less complexity, (2) designing an enhanced indirect centralized correntropy Kalman filter using a novel fuzzy parameter adjustment approach, which adaptively changes the estimation algorithm parameters to further improve the robustness of the proposed algorithm in the presence of dynamical maneuvers and non-Gaussian measurement noise, and (3) utilizing a pre-filtered nonlinear autoregressive network with exogenous inputs (PFNARX) designed to provide reliable interrupted positioning information during GNSS outages. The proposed SINS/GNSS system is experimentally evaluated through real-world flight tests. The obtained results reveal that the designed integration algorithm remarkably improves the estimation accuracy by adaptively tuning the parameters in an improved correntropy Kalman filter and an efficient and well-trained artificial neural network-based model in the absence of GNSS signal.

A New In-Flight Alignment Method with an Application to the Low-Cost SINS/GPS Integrated Navigation System.

The optimization-based alignment (OBA) methods, which are implemented by the optimal attitude estimation using vector observations—also called double-vectors—have proven to be effective at solving the in-flight alignment (IFA) problem. However, the traditional OBA methods are not applicable for the low-cost strap-down inertial navigation system (SINS) since the error of double-vectors will be accumulated over time due to the substantial drift of micro-electronic- mechanical system (MEMS) gyroscope. Moreover, the existing optimal estimation method is subject to a large computation burden, which results in a low alignment speed. To address these issues, in this article we propose a new fast IFA method based on modified double-vectors construction and the gradient descent method. To be specific, the modified construction method is implemented by reducing the integration interval and identifying the gyroscope bias during the construction procedure, which improves the accuracy of double-vectors and IFA; the gradient descent scheme is adopted to estimate the optimal attitude of alignment without complex matrix operation, which results in the improvement of alignment speed. The effect of different sizes of mini-batch on the performance of the gradient descent method is also discussed. Extensive simulations and vehicle experiments demonstrate that the proposed method has better accuracy and faster alignment speed than the related traditional methods for the low-cost SINS/global positioning system (GPS) integrated navigation system

An adaptive constrained type-2 fuzzy Hammerstein neural network data fusion scheme for low-cost SINS/GNSS navigation system

In low-cost micro-electro mechanical system (MEMS)-grade strap-down inertial navigation system (SINS), failure to compensate inertial sensors errors as well as un-modeled uncertainties in SINS could result in exponentially divergence in overall performance of low-cost SINSs. This study deals with the enhancement of low-cost SINS accuracy in combination of global navigation satellite system (GNSS). In this respect, a novel adaptive constrained integrated scheme for SINS/GNSS is developed based on type-2 fuzzy Hammerstein neural network (T2FHNN). To this aim, a gray-box Hammerstein neural network model are defined based on clear interpretation with the physical nature of the inertial sensors error. In addition a knowledge-based type-2 fuzzy programming extracted from inertial sensors data is also used for managing the learning rate of Hammerstein neural networks. Some vehicular real-world tests have been carried out in order to show the effectiveness and feasibility of the proposed integration scheme in the long-term performance and accuracy of the proposed navigation algorithm. The results indicate that the proposed integration algorithm improved the navigation accuracy, reliability and stability in the presence of state constraints of the stand-alone SINS during signal blockage of GNSS.

Comparison of Kalman Filters for Inertial Integrated Navigation.

The current research on integrated navigation is mainly focused on filtering or integrated navigation equipment. Studies systematically comparing and analyzing how to choose appropriate integrated filtering methods under different circumstances are lacking. This paper focuses on integrated navigation filters that are used by different filters and attitude parameters for inertial integrated navigation. We researched integrated navigation filters, established algorithms, and examined the relative merits for practical integrated navigation. Some suggestions for the use of filtering algorithms are provided. We completed simulations and car-mounted experiments for low-cost strapdown inertial navigation system (SINS) to assess the performance of the integrated navigation filtering algorithms.

A New Fast In-Motion Coarse Alignment Method for GPS-Aided Low-Cost SINS

In this paper, a new fast in-motion coarse alignment method is proposed for a low-cost strap-down inertial navigation system (SINS) aided by the global positioning system (GPS). As compared with existing dynamic attitude estimation based initial alignment methods, in the proposed method, the constant attitude matrix from initial body frame to initial navigation frame is rapidly estimated, which results in improved alignment speed. Moreover, the time-varying attitude matrix from current body frame to initial body frame and gyroscope bias are jointly estimated based on a new constructed state-space model using the dynamic attitude estimation technique, and the resultant estimate and closed-loop calculation are utilized in the construction of vector observations, which improves the accuracy of coarse alignment. Experimental results illustrate that the proposed method has faster alignment speed and better alignment accuracy than existing state-of-the-art methods for GPS-aided low-cost SINS.

Fuzzy adaptive integration scheme for low-cost SINS/GPS navigation system

Abstract Due to weak stand-alone accuracy as well as poor run-to-run stability of micro-electro mechanical system (MEMS)-based inertial sensors, special approaches are required to integrate low-cost strap-down inertial navigation system (SINS) with global positioning system (GPS), particularly in long-term applications. This paper aims to enhance long-term performance of conventional SINS/GPS navigation systems using a fuzzy adaptive integration scheme. The main concept behind the proposed adaptive integration is the good performance of attitude-heading reference system (AHRS) in low-accelerated motions and its degradation in maneuvered or accelerated motions. Depending on vehicle maneuvers, gravity-based attitude angles can be intelligently utilized to improve orientation estimation in the SINS. Knowledge-based fuzzy inference system is developed for decision-making between the AHRS and the SINS according to vehicle maneuvering conditions. Inertial measurements are the main input data of the fuzzy system to determine the maneuvering level during the vehicle motions. Accordingly, appropriate weighting coefficients are produced to combine the SINS/GPS and the AHRS, efficiently. The assessment of the proposed integrated navigation system is conducted via real data in airborne tests.

Novel In-flight Coarse Alignment of Low-cost Strapdown Inertial Navigation System for Unmanned Aerial Vehicle Applications

Novel In-flight Coarse Alignment of Low-cost Strapdown Inertial Navigation System for Unmanned Aerial Vehicle Applications

Initial Alignment by Attitude Estimation for Strapdown Inertial Navigation Systems

This paper derives a novel initial alignment method for the strapdown inertial navigation system (SINS), which transforms the attitude alignment into an attitude estimation problem. The process model of the proposed initial alignment method by attitude estimation is established by decomposition of the attitude matrix. The measurement model is constructed based on a generalized velocity integration formula that can integrate the inertial measurements over certain fixed time intervals. The contributions of the work presented here are twofold. First, the attitude estimation-based structure enables the proposed method to estimate the gyroscope biases other than the attitude quaternion, which is celebrated for the low-cost SINS. The second is the application of the proposed generalized velocity integration formula to attenuate the accumulated errors in vector observations caused by the traditional velocity integration formula. Experimental road tests are performed with a low-cost SINS, which validate the efficacy of the proposed method.

The Initial Alignment Method for Low-Cost Strapdown Inertial Navigation System Based on Kalman Filter

In view of the problem that the sensor accuracy of low-cost SINS is very low and the system is difficult to achieve autonomous initial alignment, a initial alignment method for low-cost SINS based on Kalman filter was presented. Firstly, accomplish the system coarse alignment by the aid of electronic compass; on this basis, adopt modern Kalman filter technology to establish accurate error models, and estimate the platform error angle by the statistical property of the system noise and measurement noise. The simulation results shows that the initial alignment method could provide precise initial condition for the low-cost SINS with short solving time, high precision and rapid convergence, and has strong value for practical applications.

English

This paper presents error modelling and error analysis of microelectromechnical systems (MEMS) inertial measurement unit (IMU) for a low-cost strapdown inertial navigation system (INS). The INS consists of IMU and navigation processor. The IMU provides acceleration and angular rate of the vehicle in all the three axes. In this paper, errors that affect the MEMS IMU, which is of low cost and less volume, are stochastically modelled and analysed using Allan variance. Wavelet decomposition has been introduced to remove the high frequency noise that affects the sensors to obtain the original values of angular rates and accelerations with less noise. This increases the accuracy of the strapdown INS. The results show the effect of errors in the output of sensors, easy interpretation of random errors by Allan variance, the increase in the accuracy when wavelet decomposition is used for denoising inertial sensor raw data. Defence Science Journal, 2009, 59(6), pp.650-658 , DOI:http://dx.doi.org/10.14429/dsj.59.1571

Integrated navigation system and experiment of a low-cost and low-accuracy SINS/GPS

When SINS (strap-down inertial navigation system) is combined with GPS, the observability of the course angle is weak. Although the course angle error is improved to some extent through Kalman filtering, the course angle still assumes a divergent trend. This trend is aggravated further when using low-cost and low-accuracy SINS. In order to restrain this trend, a method that uses AHRS to substitute for SINS course angle information is put forward aimed at the hardware component characteristic of the low-cost and low-accuracy SINS including AHRS (attitude and heading reference system) and IMU (inertial measurement unit). Real static and dynamic experiments show that the method can restrain the divergent trend of the navigation system angle effectively, and the positioning accuracy is high.

GPS Aided Inertial Navigation

The Global Positioning System is an extremely accurate satellite-based navigation system which, after its completion in 1989, will provide users worldwide, 24 hour. all weather coverage. A joint research project among Boeing, Rockwell-Collins, and Northrop has been completed in which a GPS receiver was integrated with a low-cost strap-down inertial navigation system and a flight computer. A Kalman filter in the latter allows in-fight alignment and calibration of the INS. In addition, feedback from the INS to the GPS receiver improves the system's ability to reacquire satellite signals after outages. The resulting system combines the accuracy of GPS with the jamming immunity and autonomy of inertial navigation. System tests were conducted in which a Boeing owned T-33 jet aircraft was flown through known test pattern to align and calibrate the INS. Earlier tests, including tests against an airborne jammer, were conducted in a modified passenger bus.