Design and implementation of the movement gauge for rail clearance based on combination measurement method

Purpose As the infrastructure of the railway, the rail could sink or deform to different degrees due to the impact of train operation or the geological changing force for years, which will lead to the possibility that the facilities on both sides of the rail invade the rail clearance and bring hidden dangers to the safe operation of the train. The purpose of this paper is to design the gauge to measure the clearance parameters of rail. Design/methodology/approach Aiming at the problem, the gauge for clearance measurement was designed based on a combination measurement method in this paper. It consists of the measurement box and the rail measurement vehicle, which integrates a laser displacement sensor, inclination sensor, gauge sensor and mileage sensor. The measurement box was placed outside the rail vehicle. Through the design of a hardware circuit and software system, the movement measurement of the clearance parameters was realized. Findings In this paper, the measurement equations of horizontal distance and vertical height were established, the optimal solutions of the structural parameters in the equations were obtained by Levenberg–Marquardt method, then the parameter calibration problem was also solved. Originality/value The gauge has high precision; its measurement uncertainty reaches 1.27 mm. The gauge has manual and automatic working modes, which are convenient to operate and have practical popularization value.

Robust Localization in Distributed MIMO Radar Using Delay and Angle Measurements with Impulsive Noise Robust TD/AOA Localization in Impulsive Noise

This paper considers target localization using time delay (TD) and angle of arrival (AOA) measurements in distributed multiple-input multiple-output (MIMO) radar. Aiming at the problem that the localization performance of existing algorithms degrades sharply in the presence of impulsive noise, we propose a novel localization algorithm based on ℓ p -norm minimization and iteratively reweighted least squares (IRLS). Firstly, the TD and AOA measurement equations are established in the presence of zero-mean symmetric α-stable noise; then, the localization problem is transformed to a ℓ p -norm minimization problem by linearizing the measurement equations; and finally, the ℓ p -norm minimization problem is solved using IRLS by which the target position estimate is obtained, and the optimal choice of norm order p is deduced. Moreover, the Cramér–Rao bound (CRB) for target position estimation in impulsive noise is also derived, generalizing the Gaussian CRB. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in terms of localization accuracy and robustness in impulsive noise.

Unscented Particle Filter Approach for Underwater Target Tracking

This research aims to find an appropriate approach to improve system accuracy in the Doppler-bearing tracking (DBT) problem for target estimation. The topic of DBT problem is to achieve a target trajectory using bearing and frequency measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. The unscented particle filter approach is proposed to estimate the accuracy in the target motion parameters (TMP). This approach requires the observer maneuver so that the target trajectory is observable. Although in recent research papers, DBT has been proven to work efficiently without observer maneuver, TMP is unknown to the observer, and consequently, there is a need for observer maneuver. So, the algorithm is simulated with observer following s-maneuver and without any maneuver executed by the observer, and results are compared. The effectiveness of the solution and results are determined by using MATLAB simulation. It is shown that the truthfulness of the outcome is superior when the observer performs s-maneuver while compared to that without observer maneuver.

Information-Based Georeferencing of an Unmanned Aerial Vehicle by Dual State Kalman Filter with Implicit Measurement Equations

Georeferencing a kinematic Multi-Sensor-System (MSS) within crowded areas, such as inner-cities, is a challenging task that should be conducted in the most reliable way possible. In such areas, the Global Navigation Satellite System (GNSS) data either contain inevitable errors or are not continuously available. Regardless of the environmental conditions, an Inertial Measurement Unit (IMU) is always subject to drifting, and therefore it cannot be fully trusted over time. Consequently, suitable filtering techniques are required that can compensate for such possible deficits and subsequently improve the georeferencing results. Sometimes it is also possible to improve the filter quality by engaging additional complementary information. This information could be taken from the surrounding environment of the MSS, which usually appears in the form of geometrical constraints. Since it is possible to have a high amount of such information in an environment of interest, their consideration could lead to an inefficient filtering procedure. Hence, suitable methodologies are necessary to be extended to the filtering framework to increase the efficiency while preserving the filter quality. In the current paper, we propose a Dual State Iterated Extended Kalman Filter (DSIEKF) that can efficiently georeference a MSS by taking into account additional geometrical information. The proposed methodology is based on implicit measurement equations and nonlinear geometrical constraints, which are applied to a real case scenario to further evaluate its performance.

Tightly Coupled Integrated Navigation Algorithm for Hypersonic Boost-Glide Vehicles in the LCEF Frame

According to the trajectory characteristics of hypersonic boost-glide vehicles, a tightly coupled integrated navigation algorithm for hypersonic vehicles based on the launch-centered Earth-fixed (LCEF) frame is proposed. First, the strapdown inertial navigation mechanization algorithm and discrete update algorithm in the LCEF frame are introduced. Subsequently, the attitude, velocity, and position error equations of strapdown inertial navigation in the LCEF frame are introduced. The strapdown inertial navigation system/global positioning system (SINS/GPS) pseudo-range and pseudo-range rate measurement equations in the LCEF frame are derived. Further, the tightly coupled SINS/GPS integrated navigation filter state equation and the measurement equation are presented. Finally, the tightly coupled SINS/GPS integrated navigation algorithm is verified in the hardware-in-the-loop (HWIL) simulation environment. The simulation results indicate that the precision of tightly coupled integrated navigation is better than that of loosely coupled integrated navigation. Moreover, even when the number of effective satellites is less than four, tightly coupled integrated navigation functions well, thus verifying the effectiveness and feasibility of the algorithm.

An Approach to Gravity Anomaly Solution in Airborne Scalar Gravimetry

Airborne scalar gravimetry, a kinematic survey technology, is one of the most efficient techniques to acquire the gravity data in the areas where it is neither practical nor possible to make terrestrial measurement. Recently, studies have shown that the precision reaches sub-mGal. Besides the improvement of the instruments, the data processing and gravity anomaly solution algorithms are evolving consistently. This paper investigates an approach based on developing a state space model and using Kalman filtering and smoothing to determine gravity anomaly. The state space model is developed based on the gravimeter measurement equations and measurement errors equations for the specific airborne gravimetry system. The proposed method is implemented to the airborne data collected in southeast China from GT-1A with a stabilized physical platform. The solution results based on the presented model using Kalman filtering and smoothing confirm that the approach was able to implement the solution and acquire the gravity anomaly information, and the comparison between the proposed method and the traditional method indicates that a remarkable improvement in the solution precision is achieved when the proposed method is used.

Polynomial Filtering Algorithm Applied to Navigation Data Processing under Quadratic Nonlinearities in System and Measurement Equations. Part 1. Description and Comparison with Kalman Type Algorithms

The paper considers the filtering problems solved in navigation data processing under quadratic nonlinearities both in system and measurement equations. A Kalman type recursive algorithm is proposed, where the predicted estimate and gain at each step are calculated based on the assumption on the Gaussian posterior proba-bility density function of the estimated vector at the previous step and minimization of estimation error covariance matrix using a linear procedure with respect to the current measurement. The similarities between this algorithm and other Kalman type algorithms such as extended and secondorder Kalman filters are discussed. The procedure for estimating the performance and comparing the algorithms is presented.

RESEARCH AND APPLICATION OF METHODS FOR CALCULATING THE MEASUREMENT UNCERTAINTY OF INDUSTRIAL FLOWMETERS

To create conditions for the recognition of Kazakhstani certificates of conformity and the results of product tests, an assessment of measurement uncertainty is required. In this regard, there has been an increase in the practical application in Kazakhstan of the concept of measurement uncertainty. The authors developed a physical stand for a mobile complex designed to verify electromagnetic flowmeters at the place of operation. To obtain verification results, programs were developed to calculate the uncertainty of an electromagnetic flowmeters using the NI LabView software. In addition, a model for estimating the uncertainty of the relative error of flowmeters was proposed, and the measurement uncertainty was estimated using three methods: standard, Monte Carlo and Kragten. Finally, a comparative analysis was conducted on the results of the estimation of the uncertainty of the relative error of the industrial electromagnetic flowmeter. All methods give standard uncertainty values that do not exceed the acceptable range of relative error (± 1%). However, Monte Carlo method gives better results for sufficiently large number of simulations. No significant differences between the results obtained using standard and Kragten methods were discovered. The Kragten method is preferable in the absence of the need to calculate the sensitivity coefficients when calculating the total standard uncertainty, which is important for complex measurement equations.