Initial Alignment Method Research Articles

Initial Alignment Method for a Strapdown Inertial Navigation System on a Swing Base

Initial Alignment Method for a Strapdown Inertial Navigation System on a Swing Base

An Improved Initial Alignment Method for SINS/GPS Integration With Vectors Subtraction

SINS/GPS integrated navigation system has been used widely in many application situations. In the SINS/GPS integrated navigation system, the initial alignment process is carried out to determine the initial attitude of the vehicle. Based on the previous works, the position loci method is a robust initial alignment method. The outliers, which are contained in the GPS measurements, can be suppressed by the position loci. However, the initial velocity is used to construct the observation vectors in the previous works. When the errors are contained in the initial velocity measurements, the initial alignment results are bad. To address this problem, the vector subtraction operation is adopted in this paper. Then, the initial velocity is offset by this operation. Thus, the interferences, which are caused by the initial velocity errors, are suppressed. The simulation and field tests are designed to verify the performance of the proposed method. It can be found that the alignment results are similar with the methods, which contains small or no initial velocity error.

Initial alignment for point cloud registration by improved differential evolution algorithm

Aiming at the shortcomings of traditional point cloud registration algorithms, a point cloud initial alignment method based on the differential evolution algorithm is proposed. By designing an appropriate fitness function and selecting a proper evolution strategy, the probability and speed of finding the globally optimal solution are improved. Finally, the point to plane iterative closest point is used to complete the precise registration. Experimental results prove that the speed and robustness of the algorithm put forward here are improved.

An improved robust initial alignment method under geographic latitude uncertainty and external vibration and shock scenarios

Strap-down inertial navigation system (SINS) is an autonomous sensor that can be used for the initial alignment of the shearer. However, due to the lack of external auxiliary sensors in the underground coal mine, it is difficult to obtain the geographic latitude information that required by the initial alignment algorithm. Moreover, the poor working conditions in the underground coal mine will cause the SINS to be disturbed by external vibrations and shocks. Therefore, we propose a robust initial alignment method under geographic latitude uncertainty to overcome these challenges. In our proposed method, the geographic latitude is first estimated before performing initial alignment task. To estimate the geographical latitude, we project the gravity vector into the inertial coordinate frame to establish the geometric relationship between geographic latitude and gravity vector. Then, we derive the error equation of latitude self-estimation method and combine the sliding window and low-pass filter to improve the accuracy of the latitude self-estimation result. Finally, in order to reliably complete the initial alignment even if the SINS is under external vibration and shock environment, we introduce the M estimation theory into the initial alignment of the SINS, and construct the objective function that needs to be optimized based on Cauchy robust function. At the same time, the stochastic gradient descent method was used to solve the extreme value of the objective function and obtain the initial attitude quaternion. Simulation and experiments show that our proposed method has returned very satisfactory results in terms of accuracy and robust.

Carrier Doppler-based initial alignment for MEMS IMU/GNSS integrated system under low satellite visibility

Micro-electro-mechanical system (MEMS)-based inertial measurement unit (IMU) together with single-frequency Global Navigation Satellite System (GNSS) receiver provides a cost-efficient solution for outdoor navigation. An accurate initial alignment is necessary to the GNSS/IMU integrated navigation system to guarantee a good performance. Existing in-motion alignment methods mostly rely on the velocity derived from the GNSS receiver, which is hard to acquire when the receiver suffers from temporary signal blockage or deliberate interference. To mitigate the problem, we propose a carrier Doppler-based initial alignment method, which extends the availability of attitude estimation to cases where only three satellites are available. The proposed method is composed of two steps: the first step estimates a coarse initial attitude without any prior knowledge, and the second step dynamically refines the attitude estimation. Field experiment results show that the proposed method can provide attitude estimation with enough accuracy when only three satellites are available and has equivalent performance compared with the traditional optimization-based-alignment (OBA) method under general cases. The implementation of the proposed method is available on https://github.com/Wiggrim/D2R .

Quaternion Algorithm for Initial Alignment of Strapdown INS Using the A. N. Tikhonov Regularization Method

For the functioning of algorithms of inertial orientation and navigation of strapdown inertial navigation system (SINS), it is necessary to conduct a mathematical initial alignment of SINS immediately before the operation of these algorithms. An efficient method of initial alignment (not calibration!) of SINS is the method of vector matching. Its essence is to determine the relative orientation of the instrument trihedron Y (related to the unit of SINS sensors) and the reference trihedron X according to the results of measuring the projections of at least two non-collinear vectors of the axes on both trihedrons. We address the estimation of the initial orientation of the object using the method of gyrocompassing, which is a form of vector matching method. This initial alignment method is based upon using the projections of the apparent acceleration vectoraand the absolute angular velocity vectorωof the object in the coordinate systems X and Y. It is assumed that the three single-axis accelerometers and the three gyroscopes (generally speaking, the three absolute angular velocity sensors of any type), which measure the projections of the vectorsaandω, are installed along the axes of the instrument coordinate system Y. If the projections of the same vectors on the axes of the base coordinate system X are known, then it is possible to estimate the mutual orientation of X and Y trihedrons. We are solving the problem of the initial alignment of SINS for the case of a fixed base, when the accelerometers measure the projection gi(i = 1, 2, 3) of the gravity acceleration vectorg, and the gyroscopes measure the projectionsui of the vector u of angular velocity of Earth’s rotation on the body-fixed axes. The projections of the same vectors on the axes of the normal geographic coordinate system X are also estimated using the known formulas. The correlation between the projections of the vectorsuandgin X and Y coordinate system is given by known quaternion relations. In these relations the unknown variable is the orientation quaternion of the object in the X coordinate system. By separating the scalar and vector parts in the equations, we obtain an overdetermined system of linear algebraic equations (SLAE), where the unknown variable is the finite rotation vectorθ, which aligns the X and Y coordinate systems (it is assumed that there is no half-turn of the X coordinate system with respect to the Y coordinate system). Thus, the mathematical formulation of the problem of SINS initial alignment by means of gyrocompassing is to find the unknown vectorθfrom the derived overdetermined SLAE. When finding the vectorθdirectly from the SLAE (algorithm 1) and data containing measurement errors, the components of the vector q are also determined with errors (especially the component of the vectorθ, which is responsible for the courseψof an object). Depending on the pre-defined in the course of numerical experiments values of headingψ, rollϑ, pitchγangles of an object and errors of the input data (measurements of gyroscopes and accelerometers), the errors of estimating the heading angle Δψ of an object may in many cases differ from the errors of estimating the roll Δϑ and pitch Δγ angles by two-three (typically) or more orders. Therefore, in order to smooth out these effects, we have used the A. N. Tikhonov regularization method (algorithm 2), which consists of multiplying the left and right sides of the SLAE by the transposed matrix of coefficients for that SLAE, and adding the system regularization parameter to the elements of the main diagonal of the coefficient matrix for the newly derived SLAE (if necessary, depending on the value of the determinant of this matrix). Analysis of the results of the numerical experiments on the initial alignment shows that the errors of estimating the object’s orientation angles Δψ, Δϑ, Δγ using algorithm 2 are more comparable (more consistent) regarding their order.

Research on initial alignment method of vehicle-mounted SINS based on reverse navigation

In order to improve the alignment accuracy and speed of vehicle-mounted SINS, an initial alignment scheme of “inertial solidification alignment + Kalman filter fine alignment based on reverse navigation” is proposed in this paper. Firstly, an inertial solidification alignment algorithm is derived, the initial alignment problem is transformed into Wahba problem, and the optimal attitude matrix at the initial moment is obtained by singular value decomposition algorithm. The inertial solidification alignment algorithm is more concise and intuitive than the traditional alignment algorithm of geocentric inertial coordinate system. Then, we analyzed the effectiveness of rotation modulation technology to improve alignment accuracy theoretically and proved it by simulation. And it is pointed out that the application platform space is limited and the system cost is increased when using this technology. Furthermore, in order to overcome the shortcomings of using rotation modulation technology, a Kalman filter algorithm based on reverse navigation is derived. By reusing the data stored during the inertial solidification alignment, the alignment time is extended equivalently, the initial attitude error is reduced and the attitude calculation accuracy after forward navigation is improved, higher precision alignment is realized. Finally, the effectiveness of the initial alignment scheme is verified by simulation.

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.

Gradient Descent Optimization-Based SINS Self-Alignment Method and Error Analysis

In this paper, the self-alignment for stationary strapdown inertial navigation system (SINS) is formulated as an optimization problem, and two gradient descent optimization-based SINS self-alignment methods (GD1 and GD2) are proposed. The highlight lies in that two quaternion-based objective functions are firstly formulated to solve the stationary SINS self-alignment problem. Different from conventional initial alignment methods, we firstly construct a quaternion-based objective function for stationary SINS using gravity, Earth rate and local latitude information in GD1, and employs gradient descent method to achieve the minimum of the objective function. Secondly, we further improve the quaternion-based objective function in GD2 by using the measurements from IMU to represent the Earth rate instead of using the local latitude directly. Thus, GD2 method is more competent for SINS self-alignment when the local latitude information is not available. In addition, we also analyze the bias errors of accelerometer and gyroscope and the quaternion normality error for GD1 and GD2 method respectively. Moreover, based on the analysis results, a scale factor is also introduced to reduce the alignment errors of GD1 caused by gyroscope biases. Simulation and static experiment are implemented to test the performances of GD1 and GD2 method, and the results verify the accuracy and speed of the proposed methods.

A Robust Quaternion Kalman Filter Method for MIMU/GPS In-Motion Alignment

In this article, a novel robust initial alignment method for the strapdown inertial navigation system (SINS) and global positioning system (GPS) integrated system is proposed to eliminate the outliers containing in the outputs of GPS. There are two innovations in the proposed method. First, the traditional quaternion Kalman filter method is often used to estimate the constant attitude quaternion in the swaying base. However, this article devises the new quaternion state model, which can be used in the in-motion base, expanding the scope of application. Second, based on the Huber's robust theory and the in-motion quaternion Kalman filter state model, the novel algorithm called the robust quaternion Kalman filter method is devised. This method can construct the improved observation vectors to eliminate the outliers and estimate the constant attitude quaternion more accurately than the traditional in-motion method when GPS contains outliers. The experiment using the miniature inertial measurement unit (MIMU)-based SINS aided by GPS has shown the effectiveness of the proposed method.

A Newton iterative optimization combined with window loop calculation algorithm for estimating accelerometer bias based on gravitational apparent motion with excitation of swinging motion.

The initial alignment method, including the identification of inertial device error parameters, has always been a key issue in an inertial navigation system (INS). This study focuses on the error caused by the random noise of inertial devices that can be compensated by the reconstruction of gravitational apparent motion in an inertial frame under the condition of swinging motion. Attitude angles and accelerometer bias can also be estimated. However, the analysis and simulation results indicate that the existing methods cannot estimate the gyroscope bias. The accelerometer and the gyroscope bias will change over a long time, which will lead to long-term parameter identification accuracy decline or even failure. In this paper, a parameter identification algorithm based on Newton iterative optimization combined with a window loop calculation is designed to solve these problems. Simulation and turntable tests indicate that the proposed new algorithm can fulfill the initial alignment of strapdown INS under the swinging condition and estimate accelerometer bias effectively. Moreover, the new algorithm improves data utilization, which also has better time sensitivity, and the calculated alignment errors can nearly approach zero.

Rotor Position Alignment of FSTPI Based PMSM Drive Using Low Frequency Signal Injection

A PMSM drive with an incremental encoder or using sensorless control requires alignment to a predetermined rotor position (initial position) or initial rotor position detection at start-up. It is desired to lock the rotor to a known state (usually zero angle) at start-up if the initial rotor position detection is not available or difficult to obtain. In this work, a simple and proper zero angle initial rotor position alignment of four-switch three-phase (FSTP) inverter-based PMSM drive is proposed. Low-frequency voltage signal is applied to the d-axis voltage reference of the open-loop FSTPI based PMSM drive scheme without requiring complex trigonometric calculations, PI current regulators and current sensing. Therefore, fluctuated capacitor voltages at the DC-link are obtained allowing current flown through phase a locking the rotor with zero angle, properly. The proposed method has been implemented using a low-cost FSTP voltage source inverter (VSI) for PMSM drive with a floating-point TMS320F28335 DSP. The effectiveness and the feasibility of the proposed zero angle initial rotor position alignment method for PMSM driven by FSTP inverter have been demonstrated through experimental results.

Vision-based initial point alignment control for the wheel hubs in the robotic polishing system

In the robotic polishing system for the wheel hubs, initial point alignment is a very important procedure before the robot starts to polish. Since the surface shapes of the wheel hubs are complex, traditional contact sensors are not suitable for the initial point alignment. Therefore, this paper presents a vision-based initial point alignment control method due to the merits of non-contact and high precision of the vision sensor. The main procedures of the new proposed initial point alignment methods are as follows. Firstly, the center point of the wheel hub is computed based on the growth of sampled points (GSP) method. Secondly, the center lines of the spokes of the wheel hub are extracted based on the edges of the spokes. Based on the center point and the center lines of the spokes, the needed rotation angle of the wheel hub denoting the current and target initial points can be computed. Thirdly, an alignment controller is designed to align the current initial point with the target one. Finally, experiments are well conducted to demonstrate the effectiveness of the proposed initial point alignment control method for the wheel hubs.

An Initial Alignment Technology of Shearer Inertial Navigation Positioning Based on a Fruit Fly-Optimized Kalman Filter Algorithm.

The shearer is one of the core equipment of the fully mechanized coal face. The fast and accurate positioning of the shearer is the prerequisite for its memory cutting, intelligent height adjustment, and intelligent speed adjustment. Inertial navigation technology has many advantages such as strong autonomy, good concealment, and high reliability. The accurate positioning of the shearer based on inertial navigation can not only determine its operating position but also measure the direction of movement. However, when inertial navigation is used to locate the shearer in motion, the cumulative errors will occur, resulting in inaccurate positioning of the shearer. The accuracy of the initial alignment is directly related to the working precision of the inertial navigation system. In order to improve the efficiency and accuracy of initial alignment, an improved initial alignment method is proposed in this paper, which uses a fruit fly-optimized Kalman filter algorithm for initial alignment. In order to improve the filtering performance, the fruit fly-optimized Kalman filter algorithm uses an improved fruit fly algorithm to realize the adaptive optimization of system noise variance. Finally, simulation and experiments verify the effectiveness of the fruit fly-optimized Kalman filter algorithm.

Fast SINS Initial Alignment Method Based on Iterative Algorithms in Inertial Frame

Iterative algorithms based on saved data have been researched for strapdown inertial navigation system to make the best use of sensor data, and different alignment algorithms can be carried out during the iterative process to fulfil initial alignment. This paper firstly analysed two interactive processes. The forward-backward process needs to change the normal system model in the backward process and have additional error factors. While the forward-forward process is easy to be carried out without changing the system model, the transition error will also decrease the accuracy. In addition, traditional iterative methods always need to save a large amount of data and do redundant computation, which is not highly effective, especially in view of the real-time property. To address these effects, a forward-forward solution in the inertial frame based on Kalman filter is analysed. This method calculates the attitude transfer matrix in the inertial frame to deal with angular disturbances and avoids saving all the original measurement data during the process, which makes the method more stable and faster to fulfil in the real-time system. Simulation and turntable test validates the performance of the proposed method.

In-Motion Initial Alignment Using State-Dependent Extended Kalman Filter for Strapdown Inertial Navigation System

The in-motion alignment of the strapdown inertial navigation system (SINS) is still a challenging problem due to the complex environmental changes that occur during carrier movement. This article proposes a novel initial alignment method by integrating the advantages of the Special Orthogonal Group of order 3 [SO(3)] representation and the optimal attitude estimation alignment idea. In this method, the SO(3) is used to represent the initial attitude matrix, and the optimal attitude estimation alignment model is established by analyzing the influence of the sensor error during the calculation process. Next, state-dependent extended Kalman filter is investigated by mapping the SO(3) equations to the Lie algebra space, and the Lagrange formula is used to separate the state-dependent errors in the Lie algebraic filtering equation. The simulation and experimental results indicate that the proposed initial alignment method can achieve better alignment accuracy and shorter alignment time than existing methods and can fulfill the in-motion alignment tasks for SINSs in various environments.

A Position Loci-Based In-Motion Initial Alignment Method for Low-Cost Attitude and Heading Reference System

In this article, a new position loci-based in-motion initial alignment method is proposed for a low-cost attitude and heading reference system (AHRS). A new sliding-window-based position integration method is proposed to construct the position vector observations, which weakens the accumulated errors induced by the inertial measurement unit (IMU) bias errors in vector observations. Also, a new closed-loop method is proposed to jointly estimate the time-varying body attitude matrix and the unknown IMU biases and global position system (GPS) lever-arm based on the Kalman filter. The estimates of the time-varying body attitude matrix, IMU biases, and GPS lever-arm are all used to construct and compensate for the position vector observations, which improves the alignment accuracy. Both the simulation and car-mounted test results illustrate that the proposed alignment method can achieve better alignment accuracy than these existing methods for a position loci-aided low-cost AHRS.

Initial rotor alignment method for doubly salient electromagnetic machine position sensorless startup

The initial rotor alignment process plays an important role in `align and go' which is a simple, implementable and widely industrial used position sensorless startup method. To deal with the possible jitter and reverse in the initial startup process, this Letter proposes a novel initial rotor alignment method for the position sensorless startup of the doubly salient electromagnetic machine (DSEM). According to the analysis of the generated electromagnetic torque, the relation between the alignment pulse and stable locating position is studied at first. Then, to improve the initial startup performance, the alignment pulse of switching on two upper transistors and one lower transistor is proposed to be injected as the alignment pulse since the stable locating position is around the middle point of rotor sector; it owns the benefit of better selection for the initial energising phase windings. The initial sensorless startup performance is desired to be improved in terms of no jitter or reverse. The experiments of initial alignment, as well as, the full process of the sensorless startup based on `align and go' on a 12/8-pole DSEM validate the flexibility and effectiveness of the proposed method.

In brief

PAGE 560 Turkish researchers propose a novel mixture model for visible light communication systems that use multiple sources. The model provides sufficient flexibility to accurately model the received signal levels, enabling better system designs. The validity of the model has been empirically verified using real-life measurements. PAGE 542 Researchers from Nanjing, China, have developed an initial rotor alignment method for the position sensorless start-up of doubly salient electromagnetic machines. The method, “align and go”, uses electromagnetic pulses to align the rotors, looking for the most energetically favourable starting conformation. The findings are validated by experiment, showing the flexibility and effectiveness of the proposed method. SolidWorks plot of the measurement setup. Each PD shows the position respectively P1, P2 and P3. PAGE 531 A collaboration of researchers from China and Australia propose an independent path-based process recommendation algorithm to speed up biomedical process recommendation, whilst guaranteeing accuracy. The experimental results show the improvement of the proposed algorithm over current state-of-the-art. The full experiment result of “align and go” with the initial alignment pulse A+B+C−. PAGE 562 Researchers from Japan propose a novel THz-wave autocorrelator which is based on an optical delay line (ODL) and unitravelling-carrier photodiodes (UTC-PDs) for easy observation of optical waveforms. The waveform is delayed in the optical domain whilst it is autocorrelated in the THz domain. Demonstrations show the system working with sinusoidal optical waveforms with frequencies up to 300 GHz. Process models presented as a Petri net. PAGE 527 Researchers from China present a novel, 3-D printed waveguide step-twist integrated with a bandpass filter. The device has a centre frequency of 32 GHz and a bandwidth of 1 GHz. Measurements show good agreement with simulation, with insertion loss of 0.84 dB and a return loss better than 15 dB across the passband. Experimental configuration. Structural illustrations of the Ka-band step-twist.

Robust Initial Alignment for SINS/DVL Based on Reconstructed Observation Vectors

Misalignment angle will result in a considerable error for the integration of Doppler velocity log (DVL) and of Strapdown Inertial Navigation System (SINS). In this article, a robust initial alignment method for SINS/DVL is proposed to solve a practical applicable issue, which is that the outputs of DVL are often corrupted by the outliers. First, the alignment principle for SINS/DVL is summarized. Second, based on the principle of this alignment method, the apparent velocity model is investigated, and the parameters expression of the apparent velocity model are derived detailed. Using the apparent velocity model, the unknown parameters of the apparent velocity model are estimated by the developed robust Kalman filter, then the reconstructed observation vector, where the outliers are detected and isolated, is reconstructed by the estimated parameters. Based on the reconstructed observation vectors, the initial attitude is determined. Finally, the simulation and field tests are carried out to verify the performance of the proposed method. The test results are shown that the proposed method can detect and isolate the outliers effectively and get better performance than the previous work.