Dead Reckoning Navigation System Research Articles

Error analysis of dead reckoning navigation system by considering uncertainties in an underwater vehicle's sensors

Abstract In this paper, a complete introduction to the dead reckoning navigation technique is offered after a discussion of the many forms of navigation, and the benefits and drawbacks associated with each of those types of navigation. After that, the dead reckoning navigation solution is used as an option that is both low-cost and makes use of the sophisticated equations that are used by the system. Moreover, to achieve the highest level of accuracy in navigation, an investigation of navigation errors caused by dead reckoning is calculated. Employing the suggested dead reckoning navigation system, the final position of an underwater vehicle can be established with a high degree of accuracy by using experimental data (from sensors) and the uncertainties that are associated with the system. Finally, to illustrate the correctness of the dead reckoning navigation process, the system error analysis as uncertainty that was carried out using experimental data using the dead reckoning navigation technique is compared with GPS data.

Application of Weld Scar Recognition in Small-Diameter Transportation Pipeline Positioning System

In order to improve the positioning accuracy of the pipeline inspection gauge (PIG), a pipeline positioning method, based on weld location, is proposed. The position of the welding scar is recognized by wavelet transform modulus maxima (WTMM). Equidistant welding scars provide positioning references to the strap-down inertial navigation system (SINS)/dead reckoning (DR) navigation system, which is the positioning algorithm in PIG. The following improvements have been made in relation to prior research. First, we suggest a selection strategy for the optimal mother wavelet and decomposition level; based on the strategy, WTMM can recognize the collision response between the PIG and submerged weld in the burst noise for the inertial measurement unit (IMU) output. Then, characteristic position (CP), which is the site of the weld scar, and non-holonomic constraints are utilized to decrease the position and the attitude error. By doing such, the SINS/DR/CP algorithm is proposed. The positioning error of the modified algorithm is 0.129% in the experiment, which performs better than other algorithms.

CNN-BASED PLACE RECOGNITION TECHNIQUE FOR LIDAR SLAM

Abstract. Place recognition or loop closure is a technique to recognize landmarks and/or scenes visited by a mobile sensing platform previously in an area. The technique is a key function for robustly practicing Simultaneous Localization and Mapping (SLAM) in any environment, including the global positioning system (GPS) denied environment by enabling to perform the global optimization to compensate the drift of dead-reckoning navigation systems. Place recognition in 3D point clouds is a challenging task which is traditionally handled with the aid of other sensors, such as camera and GPS. Unfortunately, visual place recognition techniques may be impacted by changes in illumination and texture, and GPS may perform poorly in urban areas. To mitigate this problem, state-of-art Convolutional Neural Networks (CNNs)-based 3D descriptors may be directly applied to 3D point clouds. In this work, we investigated the performance of different classification strategies utilizing a cutting-edge CNN-based 3D global descriptor (PointNetVLAD) for place recognition task on the Oxford RobotCar dataset.

Frogman Self-Navigation Method Based on Virtual Transponder Array and Dead Reckoning

Using single transponder ranging (STR) information to aid positioning satisfies the needs of frogman autonomous positioning in waters emergency rescue, where miniaturized and low-cost devices are preferred. On this basis, a frogman self-navigation method based on virtual transponder array (VTA) and dead reckoning (DR) is proposed in this article, to solve the problem that calculation results of DR still accumulate with time when STR information is taken as the external measurement. In the proposed method, firstly, by constructing the VTA with depth information constraint, frogman autonomous positioning under the condition of single transponder configuration is realized. Then, to combine the characteristics that DR positioning data is smooth and short-term stable, and that VTA positioning error is not cumulative, the DR navigation system is used to describe frogman's motion law, the frogman positioning coordinates that calculated by VTA are taken as external measurement and the Kalman filter is designed, which solves the problem of DR accumulated errors. Compared to traditional STR-DR method, the proposed VTA-DR method further improves the accuracy and stability of frogman navigation and positioning. Finally, based on the high-precision three-dimensional motion capture system, the semi-physical simulation experimental environment is built to verify the proposed method. The experimental results under different tracks indicate that the average total location error of VTA-DR method is 0.149m, which is reduced by 53.5% compared with STR-DR method. The proposed VTA-DR method can better suppress the accumulation of positioning errors and has better positioning accuracy and robustness.

A Low-Cost Underground Garage Navigation Switching Algorithm Based on Kalman Filtering.

Modern parking lots have gradually developed into underground garages to improve the efficient use of space. However, the complex design of parking lots also increases the demands on vehicle navigation. The traditional method of navigation switching only uses satellite signals. After the Position Dilution Of Precision (PDOP) of satellite signals is over the limit, vehicle navigation will enter indoor mode. It is not suitable for vehicles in underground garages to switch modes with a fast-response system. Therefore, this paper chooses satellite navigation, inertial navigation, and the car system to combine navigation. With the condition that the vehicle can freely travel through indoor and outdoor environments, high-precision outdoor environment navigation is used to provide the initial state of underground navigation. The position of the vehicle underground is calculated by the Dead Reckoning (DR) navigation system. This paper takes advantage of the Extended Kalman Filter (EKF) algorithm to provide two freely switchable navigation modes for vehicles in ground and underground garages. The continuity, robustness, fast response, and low cost of the indoor and outdoor switching navigation methods are verified in real-time systems.

A Low-Cost Dead Reckoning Navigation System for an AUV Using a Robust AHRS: Design and Experimental Analysis

In navigation and motion control of an autonomous vehicle, estimation of attitude and heading is an important issue, especially when the localization sensors such as global positioning system (GPS) are not available and the vehicle is navigated by the dead reckoning (DR) strategies. In this paper, based on a new modeling framework, an extended Kalman filter (EKF) is utilized for estimation of attitude, heading, and gyroscope sensor bias using a low-cost microelectromechanical system (MEMS) inertial sensor. The algorithm is developed for accurate estimation of attitude and heading in the presence of external disturbances including external body accelerations and magnetic disturbances. In this study, using the proposed attitude and heading reference system (AHRS) and an identified surge dynamic model of an autonomous underwater vehicle, a low-cost model-aided DR navigation system has been designed for an AUV. The proposed algorithm application is evaluated by experimental tests in different acceleration bound and existence of external magnetic disturbances for an AUV. The results indicate that the roll, pitch, and heading are estimated by mean value errors about 0.35°, 0.29°, and 1.27°, respectively. Moreover, they indicate that a relative navigation error about 8% of the traveling distance can be achieved using the developed approach in during GPS outages.

LeapFrogging : A Technique for Accurate Long‐Distance Ground Navigation and Positioning Without GPS

We present a dead-reckoning navigation system that combines elements of traditional map and compass orienteering techniques with concepts from collaborative positioning and field surveying. Using two or more people, and with various technologies to perform ranging and/or directional measurements, the users take turns moving from a known starting position. The LeapFrogging system tracks the measurements and is able to reconstruct the estimated path and provide a current position estimate to the users. Various technologies and movement schemes that can be utilized for LeapFrogging are explained and discussed. Test results from several field data collections are provided and demonstrate that positional errors are usually on the order of 1–3 percent positional error per distance traveled, and sometimes reach as low as 0.5 to 1.0 percent error. A man-portable LeapFrogging system could be useful for teams of soldiers, search-and-rescue, and hikers in regions where GPS may be degraded, denied, or temporarily unavailable. Copyright © 2018 Institute of Navigation

Statistics of an autoregressive correlated random walk along a return path

A closed-form analytical solution is derived for the statistical outcome of a random walk along a return path. The random walk is generated from the cumulative sum of correlated samples in a Gaussian-distributed autoregressive sequence. The outcome exhibits a smaller variance compared with a one-way path of equivalent length due to cancellation of correlated steps along the return leg. Furthermore, the variance decreases towards zero as the correlation coefficient approaches unity. An example application for this general result is the modelling of cumulative errors in dead-reckoning navigation systems, e.g. Doppler velocity log-aided inertial navigation systems used commonly on underwater vehicles. In this particular application, it can be used to express and quantify the natural cancellation of correlated error components between subsequent opposing legs in a typical ‘lawnmower’ survey pattern.

Experimental analysis of a low-cost dead reckoning navigation system for a land vehicle using a robust AHRS

In navigation and motion control of an autonomous vehicle, estimation of attitude and heading is an important issue especially when the localization sensors such as GPS are not available and the vehicle is navigated by the dead reckoning (DR) strategies. In this paper, based on a new modeling framework an Extended Kalman Filter (EKF) is utilized for estimation of attitude, heading and gyroscope sensor bias using a low-cost MEMS inertial sensor. The algorithm is developed for accurate estimation of attitude and heading in the presence of external disturbances including external body accelerations and magnetic disturbances. In this study using the proposed attitude and heading reference system (AHRS) and an odometer sensor, a low-cost aided DR navigation system has been designed. The proposed algorithm application is evaluated by experimental tests in different acceleration bound and existence of external magnetic disturbances for a land vehicle. The results indicate that the roll, pitch and heading are estimated by mean value errors about 0.83%, 0.68% and 1.13%, respectively. Moreover, they indicate that a relative navigation error about 3% of the traveling distance can be achieved using the developed approach in during GPS outages.

Bias Drift Estimation for MEMS Gyroscope Used in Inertial Navigation

Abstract MEMS gyroscopes can provide useful information for dead-reckoning navigation systems if suitable error compensation algorithm is applied. If there is information from other sources available, usually the Kalman filter is used for this task. This work focuses on improving the performance of the sensor if no other information is available and the integration error should be kept low during periods of still (no movement) operation. A filtering algorithm is proposed to follow bias change during sensor operation to reduce integration error and extend time between successive sensor calibrations. The advantage of the proposed solution is its low computational complexity which allows implementing it directly in the micro-controller of controlling the MEMS gyroscope. An intelligent sensor can be build this way, suitable for use in control systems for mobile platforms. Presented results of a simple experiment show the improvement of the angle estimation. During the 12 hours experiment with a common MEMS sensor and no thermal compensation, the maximum orientation angle error was below 8 degrees.

A RFT Based Regular Position Correction Algorithm for Pedestrian Dead Reckoning Navigation System

Pedestrian dead reckoning (PDR) is one of the most popular techniques recognizing a pedestrian trajectory by real-time motion parameters obtained from motion sensors embedded in smart-phones. However, PDR has an inherent disadvantage of susceptibility to error accumulation. Radio Frequency Tomography (RFT) is a fairly new technology to get the user positioning information utilizing the received signal strength (RSS) fluctuant of line-of-sight communication paths (LOSP) in the wireless sensor network. In this paper, based on RFT, we estimate crossing point (CP) on the LOSP, which is for correcting the user’s current position and mitigating the error accumulation of PDR based positioning technique. We do computer simulations to evaluate the positioning scheme jointing PDR and RFT. The positioning performance shows that our scheme can achieve a finer accuracy around 1 meter.

Performance of networked dead reckoning navigation system

Networked navigation systems aided by cooperative information sharing are evaluated using flight data from seven uninhabited aerial vehicles. Decentralized estimation architectures are evaluated, and their performance is compared against centralized implementations. It is shown that a flexible covariance intersection-based decentralized implementation is effective, at a fraction of the resources required for a centralized implementation, if resources are put toward ensuring the continued presence of one or more high-quality users in the community.

A novel separation and calibration method for DVL and compass error in dead reckoning navigation systems

The scale factor error of the Doppler velocity log (DVL) and the heading angle error of a compass are so integrated in dead reckoning (DR) navigation systems that it is difficult to separate them. This paper aims to solve this problem by putting forward an online separation and calibration method for and based on an ‘arc and linear’ trajectory. This method introduces the high-accuracy location information of a long base line (LBL) acoustic positioning system. At first, the relationship between the displacements on the ‘arc’ trajectory in directions of east and north, output by the LBL and DR systems, serves to judge the carrier direction and calibrate . And then by compensating , the displacement on the ‘linear’ trajectory is used to calibrate . Finally, a semi-physical simulation experiment is conducted to test and verify this calibration method to see how effective and accurate it is. Experimental results show that after calibration the residual error ratios of and are 8.24% and 3.70% respectively. Therefore, online calibration of and is realized effectively. What’s more, when the DR system is working alone in 400 s, this method reduces position error by up to 93.39%, from 18.91 m to 1.25 m.

Daily Living Movement Recognition for Pedestrian Dead Reckoning Applications

Nowadays, activity recognition is a central topic in numerous applications such as patient and sport activity monitoring, surveillance, and navigation. By focusing on the latter, in particular Pedestrian Dead Reckoning navigation systems, activity recognition is generally exploited to get landmarks on the map of the buildings in order to permit the calibration of the navigation routines. The present work aims to provide a contribution to the definition of a more effective movement recognition for Pedestrian Dead Reckoning applications. The signal acquired by a belt-mounted triaxial accelerometer is considered as the input to the movement segmentation procedure which exploits Continuous Wavelet Transform to detect and segment cyclic movements such as walking. Furthermore, the segmented movements are provided to a supervised learning classifier in order to distinguish between activities such as walking and walking downstairs and upstairs. In particular, four supervised learning classification families are tested: decision tree, Support Vector Machine,k-nearest neighbour, and Ensemble Learner. Finally, the accuracy of the considered classification models is evaluated and the relative confusion matrices are presented.

Ograniczanie błędów nawigacji dla odwiertów kierunkowych i rurociągów podziemnych

Knowledge of the trajectory of the borehole is necessary in order to allow for the control of the drill so as to achieve the established process bore. Similarly, in the case of pipelines for transferring fuels, they should be monitored. This is possible thanks to the use of methods of reducing errors in dead reckoning navigation systems with special taking into account of proper set of the initial conditions of navigation.

On-Board Applicability of Mems-Based Autonomous Navigation System on Agricultural Aircrafts

In practical terms of agricultural flights, the success of the mission has been largely determined by the airmanship, experience and visual reference of the pilot. By using microelectro-mechanical system (MEMS) devices, a complex onboard system can be designed which increases flight safety and improves the efficiency of pest control. With the help of dead reckoning navigation system using MEMS accelerometers and gyroscopes, the spatial attitude and geographic location of the aircraft can be obtained. By measuring the surrounding local air pressure and applying the mathematical model of particular aircraft types´ vortex and stream systems and the properties of the pesticides deployed the accuracy of the crop spraying can be improved while the spraying device can be operated automatically in a control loop. With real-time data processing the pesticide coverage is more uniform, the overlap spraying of the fields can be avoided, and the lateral distance limit between fields and urban built-up areas can be better controlled. It follows that the environmental load can be decreased and the usage of chemicals and pesticides can be optimized.

Small Autonomous Underwater Vehicle Navigation System Based on Adaptive UKF Algorithm

Ocean current disturbance and attitude,heading errors can cause uncertain navigation system model error.To solve the above problem,an adaptive unscented Kalman filter (AUKF) with model error is designed for a small autonomous underwater vehicle s (SAUV) dead reckoning (DR) navigation system.Firstly,three-dimensional motion of SAUV continuous time model is designed.Then,the proposed AUKF algorithm is deduced according to maximum a posterior (MAP) theory.Finally,simulation results show that the algorithm can overcome the model error caused by disturbance currents and attitude,and heading errors.Compared with the conventional UKF algorithm,the filter precision of the SAUV s DR navigation system in complex sea state is improved a lot by adopting the proposed algorithm.

A Robust Map-Match Algorithm for DR Correction

In this paper, a robust correction algorithm for Dead Reckoning (DR) navigation system is proposed based on digital map. The DR system here contains an IMU unit, in which 3 Ring Laser Gyros (RLG) and 3 accelerometers are consisted, providing azimuth angle, and an odometer that can provide mileage increament. An improved Map-Match algorithm is designed and implemented, which composes a “road correction” unit and a “road projection” unit based on geometry theory. To enhance the robustness of the system, a new communication protocol is designed. Based on the information provided by digital map, the DR system can not only corrects the position output, but also estimates the odometer scale factor error with a newly designed online calibration algorithm. Road test is conducted and the results have testified the effectiveness of the algorithm.

Onboard Estimation and Classification of a Railroad Curvature

When performing tests on a rail vehicle, it is often necessary to categorize the target data according to the characteristics of the railroad plane geometry. In this paper, a method to classify and identify the railroad plane geometry is considered, employing railroad curvature readings formed by onboard sensor data. The aim is to extract the characteristics of the railroad track to identify and categorize different segments of the track. The railroad curvature is modeled as a first-order piecewise linear polynomial representing sections of straight tracks, transition curves, and circular curves along the railroad. The sensor data are preprocessed in a Global Positioning System-aided dead reckoning navigation system to debias the curvature readings. Subsequently, the noise in the curvature readings is suppressed by nonlinear filtering techniques. Furthermore, the observed curvatures are processed with a linear filter by minimizing a discounted least-squares criterion, yielding the final estimate of the railroad curvature and its rate of change, which further on are utilized to form a detector where the position of a trend change in curvature measurements is located. The result from the presented method has been compared against database values on plane geometry from Banverkets Information System-a system belonging to the Swedish Rail Administration office. The reported accuracy of detecting a change in the railroad curvature has varied in the range of ±7 m in difference between the position entries in the curvature database and the positions found with the developed method.

Recurrent neural network for vehicle dead-reckoning

For vehicle integrated navigation systems, real-time estimating states of the dead reckoning (DR) unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteristics. Compared with the well known, extended Kalman filter (EKF), a recurrent neural network is proposed for the solution, which not only improves the location precision and the adaptive ability of resisting disturbances, but also avoids calculating the analytic derivation and Jacobian matrices of the nonlinear system model. To test the performances of the recurrent neural network, these two methods are used to estimate the state of the vehicle's DR navigation system. Simulation results show that the recurrent neural network is superior to the EKF and is a more ideal filtering method for vehicle DR navigation.