Open-sky Environments Research Articles

GNSS/INS/ODO/wheel angle integrated navigation algorithm for an all-wheel steering robot

To suppress the positioning error of wheeled robots in global navigation satellite system (GNSS) denial environments, kinematic constraints should be fully utilized. However, many wheeled robots have independent steering mechanisms for each wheel to move more flexibly, which do not meet the nonholonomic constraint in the vehicle frame. Hence, the conventional GNSS/inertial navigation system (INS)/ODO (odometer) integrated navigation algorithm (GIOW algorithm) is no longer suitable. We propose a GIOW algorithm to meet the need for all-wheel steering robot positioning. In the proposed algorithm, odometer speed and wheel angle are employed together to construct a kinematic constraint for wheeled robots, which can constrain the rapid drifting error of INS. Moreover, the odometer scale factor and wheel angle error are augmented into the error state of the extended Kalman filter to be estimated and compensated online. Field tests were carried out in an open-sky environment with a wheeled robot, which can run in both corner steering mode and all-wheel steering mode. The experimental results showed that the proposed algorithm can be applied to not only the corner steering motion model but also the all-wheel steering motion model. The accuracy of the proposed algorithm was almost the same as that of the conventional GNSS/INS/ODO algorithm in the corner steering motion model. In the all-wheel steering motion model, the accuracy of the proposed algorithm was maintained.

An uncombined triple-frequency user implementation of the decoupled clock model for PPP-AR

Precise point positioning (PPP) has proved its capacity to provide centimetre-level position solutions in open sky environments. However, the technique still suffers from relatively long initial convergence times. Research has proved the potential of ambiguity resolution (AR) to reduce the convergence time and three main methods are used to perform AR: the fractional cycle bias method, the decoupled clock model (DCM) and the integer recovery clock method. This paper focuses on the DCM and expands it at the user side to better fit the current context. Seeing as multi-frequency processing is proving to improve PPP performance, the classical DCM model is extended from a combined dual-frequency model to an uncombined triple-frequency one. The user implementation is tested on 1400, 3-h-long datasets from global IGS stations for 1 week with the Galileo constellation in both static and kinematic modes. First, some of the model-specific parameters are plotted and the estimated receiver biases are visualized. Then, dual- and triple-frequency PPP-AR results are shown. In both frequency modes, the convergence time and accuracy of the float solutions are improved with AR. In the dual-frequency case, the 100-percentile mean convergence time reduces from 19 min for the float solution to 14 min for the fixed solution, and the horizontal root mean square error improves 2.7 to 1.1 cm. In the triple-frequency case, the convergence time reduces from 17.5 min for the float solution to 9.5 min for the fixed solution, and the accuracy improves from 2.6 to 1.0 cm. These results show a minimal improvement in the accuracy between the dual-frequency and triple-frequency AR solutions, and a significant 40–50% improvement in the convergence time. Future work includes applying these developments to multi-constellation PPP-AR, which would further reduce the convergence time.

Conditioning and PPP processing of smartphone GNSS measurements in realistic environments

Smartphones typically compute position using duty-cycled Global Navigation Satellite System (GNSS) L1 code measurements and Single Point Positioning (SPP) processing with the aid of cellular and other measurements. This internal positioning solution has an accuracy of several tens to hundreds of meters in realistic environments (handheld, vehicle dashboard, suburban, urban forested, etc.). With the advent of multi-constellation, dual-frequency GNSS chips in smartphones, along with the ability to extract raw code and carrier-phase measurements, it is possible to use Precise Point Positioning (PPP) to improve positioning without any additional equipment. This research analyses GNSS measurement quality parameters from a Xiaomi MI 8 dual-frequency smartphone in varied, realistic environments. In such environments, the system suffers from frequent phase loss-of-lock leading to data gaps. The smartphone measurements have low and irregular carrier-to-noise (C/N0) density ratio and high multipath, which leads to poor or no positioning solution. These problems are addressed by implementing a prediction technique for data gaps and a C/N0-based stochastic model for assigning realistic a priori weights to the observables in the PPP processing engine. Using these conditioning techniques, there is a 64% decrease in the horizontal positioning Root Mean Square (RMS) error and 100% positioning solution availability in sub-urban environments tested. The horizontal and 3D RMS were 20 cm and 30 cm respectively in a static open-sky environment and the horizontal RMS for the realistic kinematic scenario was 7 m with the phone on the dashboard of the car, using the SwiftNav Piksi Real-Time Kinematic (RTK) solution as reference. The PPP solution, computed using the YorkU PPP engine, also had a 5–10% percentage point more availability than the RTK solution, computed using RTKLIB software, since missing measurements in the logged file cause epoch rejection and a non-continuous solution, a problem which is solved by prediction for the PPP solution. The internal unaided positioning solution of the phone obtained from the logged NMEA (The National Marine Electronics Association) file was computed using point positioning with the aid of measurements from internal sensors. The PPP solution was 80% more accurate than the internal solution which had periodic drifts due to non-continuous computation of solution.

Autonomous Ground Vehicle Path Planning in Urban Environments Using GNSS and Cellular Signals Reliability Maps: Models and Algorithms

In this article, autonomous ground vehicle (AGV) path planning is considered. The AGV is assumed to be equipped with receivers capable of producing pseudorange measurements to overhead global navigation satellite systems (GNSS) satellites and to cellular base stations in its environment. Parameters of the cellular pseudoranges related to the transmitter clock bias are estimated in an initialization step in an open-sky environment. The AGV fuses these pseudoranges to produce an estimate about its own states. The AGV is also equipped with a three-dimensional building map of the environment. Starting from a known starting point, the AGV desires to reach a known target point by taking the shortest distance, while minimizing the AGV's position estimation error and guaranteeing that the AGV's position estimation uncertainty is below a desired threshold. Toward this objective, a so-called signal reliability map is first generated, which provides information about regions where large errors due to poor GNSS line-of-sight or cellular signal multipath are expected. The vehicle uses the signal reliability map to calculate the position mean-squared error (MSE). An analytical expression for the AGV's state estimates is derived, which is used to find an upper bound on the position bias due to multipath. An optimal path planning generation approach, which is based on Dijkstra's algorithm, is developed to optimize the AGV's path while minimizing the path length and position MSE, subject to keeping the position estimation uncertainty and position estimation bias due to multipath below desired thresholds. The path planning approach yields the optimal path together with a list of feasible paths and reliable GNSS satellites and cellular base stations to use along these paths.

A Comparative Analysis on Inter-frequency Data Quality of Quad-constellation GNSS on a Smartphone

This paper comprehensively analyzes the inter-frequency data quality of the quad-constellation Global Navigation Satellite System (GNSS) of GPS, GLONASS, BDS and Galileo on a smartphone. A series of indices, i.e. the number of visible satellites, data integrity rate, multipath, carrier-to-noise ratio (C/No), cycle-slip ratio and observation residuals, are employed to evaluate the data quality with a comparison between different constellations and frequencies. Experiments were conducted using the firstly released dual-frequency smartphone of Xiaomi Mi8. The results show that the GPS and BDS exhibit the best tracking performance in an open-sky environment with an average of 7 observed satellites at each epoch, which is 3 or 4 satellites more than the Galileo and GLONASS. In addition, the GPS data integrity rate is higher than the other constellations by about 20%-25%. The GPS suffers a multipath effect two times larger than the Galileo on the L1/E1 frequencies, but they are almost equal on the L5/E5a frequencies. For all four constellations, the C/No is mostly concentrated at 20-35 dB-Hz. Further, the C/No on the L1/E1 frequencies increases by 3-4 dB-Hz over the L5/E5a frequencies. The GLONASS observations exhibit the most serious cycle slip occurrence rate at a ratio of 100, which is significantly larger than the other constellations. Regarding the residuals, the phase RMS residuals for all four constellations are at a few millimeters, whereas the pseudorange residuals of GLONASS are the most prominent with an RMS of over 6 m, which is 3-4 times larger than the other constellations. The precise point positioning (PPP) results show that the convergence time and positioning accuracy can be effectively improved by adding GPS and Galileo data at L5/E5a.

Real-time GNSS precise point positioning using improved robust adaptive Kalman filter

Multi-constellation GNSS precise point positioning (PPP) typically uses the extended Kalman filter (EKF) for kinematic applications. Unfortunately, the obtained positioning accuracy in this approach is prone to errors caused by measurement outliers and the system’s dynamic model. An adaptive robust Kalman filter (RKF) was recently developed to mitigate these errors. However, RKF uses empirical values as detection thresholds for the outliers, which requires the measurements to be from the same constellation and of equal precision to obtain an optimal PPP solution. The classification robust adaptive Kalman filter (CAKF) has subsequently been developed to deal with measurements of different precisions, namely pseudorange and carrier-phase measurements. This paper proposes a real-time GPS/Galileo PPP system, which employs a modified version of CAKF called the Improved Robust adaptive Kalman Filter (IRKF). The positioning performance of GPS/Galileo PPP through the IRKF is initially verified in comparison with those obtained through the EKF, RKF, and CAKF using the Centre for Orbit Determination in Europe (CODE) final orbit and clock products in both of static and kinematic modes. The real-time GPS/Galileo PPP solution through the IRKF is then assessed in comparison with its near-real-time counterpart. The results indicate that when the IRKF approach is utilised, the positioning accuracy is significantly improved and the convergence behaviour is enhanced compared with results from EKF, conventional RKF, and CAKF. In the real-time mode, centimeter-level horizontal positioning accuracy is achieved under an open sky environment, while decimeter-level horizontal positioning accuracy is achieved under a challenging environment. On the other hand, decimeter-level accuracy is achieved for the vertical positioning component under all environmental scenarios. Moreover, the positioning accuracy of the real-time solution is comparable to the near-real-time counterpart.

Performance evaluation of real-time tightly-coupled GNSS PPP/MEMS-based inertial integration using an improved robust adaptive Kalman filter

Abstract Typically, the extended Kalman filter (EKF) is used for tightly-coupled (TC) integration of multi-constellation GNSS PPP and micro-electro-mechanical system (MEMS) inertial navigation system (INS) to provide precise positioning, velocity, and attitude solutions for ground vehicles. However, the obtained solution will generally be affected by both of the GNSS measurement outliers and the inaccurate modeling of the system dynamic. In this paper, an improved robust adaptive Kalman filter (IRKF) is adopted and used to overcome the effect of the measurement outliers and dynamic model errors on the obtained integrated solution. A real-time IRKF-based TC GPS+Galileo PPP/MEMS-based INS integration algorithm is developed to provide precise positioning and attitude solutions. The pre-saved real-time orbit and clock products from the Centre National d’Etudes Spatials (CNES) are used to simulate the real-time scenario. The performance of the real-time IRKF-based TC GNSS PPP/INS integrated system is assessed under open sky environment, and both of simulated partial and complete GNSS outages through two ground vehicular field trials. It is shown that the real-time TC GNSS PPP/INS integration through the IRKF achieves centimeter-level positioning accuracy under open sky environments and decimeter-level positioning accuracy under GNSS outages that range from 10 to 60 seconds. In addition, the use of IRKF improves the positioning accuracy and enhances the convergence of the integrated solution in comparison with the EKF. Furthermore, the IRKF-based integrated system achieves attitude accuracy of 0.052°, 0.048°, and 0.165° for pitch, roll, and azimuth angles, respectively. This represents improvement of 44 %, 48 %, and 36 % for the pitch, roll, and azimuth angles, respectively, in comparison with the EKF-based counterpart.

Lane-Level Map-Matching Method for Vehicle Localization Using GPS and Camera on a High-Definition Map.

Accurate vehicle localization is important for autonomous driving and advanced driver assistance systems. Existing precise localization systems based on the global navigation satellite system cannot always provide lane-level accuracy even in open-sky environments. Map-based localization using high-definition (HD) maps is an interesting method for achieving greater accuracy. We propose a map-based localization method using a single camera. Our method relies on road link information in the HD map to achieve lane-level accuracy. Initially, we process the image—acquired using the camera of a mobile device—via inverse perspective mapping, which shows the entire road at a glance in the driving image. Subsequently, we use the Hough transform to detect the vehicle lines and acquire driving link information regarding the lane on which the vehicle is moving. The vehicle position is estimated by matching the global positioning system (GPS) and reference HD map. We employ iterative closest point-based map-matching to determine and eliminate the disparity between the GPS trajectories and reference map. Finally, we perform experiments by considering the data of a sophisticated GPS/inertial navigation system as the ground truth and demonstrate that the proposed method provides lane-level position accuracy for vehicle localization.

Characteristics Analysis of Raw Multi-GNSS Measurement from Xiaomi Mi 8 and Positioning Performance Improvement with L5/E5 Frequency in an Urban Environment

Achieving continuous and high-precision positioning services via smartphone under a Global Navigation Satellite System (GNSS)-degraded environment is urgently demanded by the mass market. In 2018, Xiaomi launched the world’s first dual-frequency GNSS smartphone, Xiaomi Mi 8. The newly added L5/E5 signals are more precise and less prone to distortions from multipath reflections. This paper discusses the characteristics of raw dual-frequency GNSS observations from Xiaomi Mi 8 in urban environments; they are characterized by high pseudorange noise and frequent signal interruption. The traditional dual-frequency ionosphere-free combination is not suitable for Xiaomi Mi 8 raw GNSS data processing, since the noise of the combined measurements is much larger than the influence of the ionospheric delay. Therefore, in order to reasonably utilize the high precision carrier phase observations, a time differenced positioning filter is presented in this paper to deliver continuous and smooth navigation results in urban environments. The filter first estimates the inter-epoch position variation (IEPV) with time differenced uncombined L1/E1 and L5/E5 carrier phase observations and constructs the state equation with IEPV to accurately describe the user’s movement. Secondly, the observation equations are formed with uncombined L1/E1 and L5/E5 pseudorange observations. Then, kinematic experiments in open-sky and GNSS-degraded environments are carried out, and the proposed filter is assessed in terms of the positioning accuracy and solution availability. The result in an open-sky environment shows that, assisted with L5/E5 observations, the root mean square (RMS) of the stand-alone horizontal and vertical positioning errors are about 1.22 m and 1.94 m, respectively, with a 97.8% navigation availability. Encouragingly, even in a GNSS-degraded environment, smooth navigation services with accuracies of 1.61 m and 2.16 m in the horizontal and vertical directions are obtained by using multi-GNSS and L5/E5 observations.

In-Lane Localization and Ego-Lane Identification Method Based on Highway Lane Endpoints

The low-cost global navigation satellite systems combined with an inertial navigation system (GNSS/INS) used most frequently for vehicle localization show errors up to 10 m, approximately, even in open-sky environments such as highways. To reduce this error on highways, this paper proposes a localization method based on lane endpoints. Since a lane endpoint frequently appears on a road and can be detected in close proximity even by a low-cost monocular camera, it is a very useful landmark for precise localization. However, the lane width is generally less than 3.5 m, and the localization error from the GNSS is about 10 m. Therefore, if an ego-lane is not identified, the lane endpoints detected in an ego-lane can be falsely corresponded to the lane endpoints in the other lane of a map. This paper proposes an in-lane localization method that uses lane endpoints, the relation between a camera and a road, and the estimated vehicle’s orientation from a map. In addition, this paper proposes an ego-lane identification method that generates a hypothesis about an ego vehicle position per lane by using the proposed in-lane localization method and verifies each hypothesis by the projection of lane endpoints and an additional landmark such as a road sign. The average error of the proposed in-lane localization method is 0.248 m on highways. The success rate of the proposed ego-lane identification method is 99.28% by one trial and reaches 100% by fusing the results.

Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications

In recent decades Global Positioning Systems (GPS) have become a ubiquitous tool to support navigation. Traditional GPS has an error in the order of 10–15 m, which is adequate for many applications (e.g., vehicle navigation) but for many robotics applications lacks required accuracy. In this paper we describe a technique, FAGPS (Fiducial Augmented Global Positioning System) to periodically use fiducial markers to lower the GPS drift, and hence for a small time-period have a more accurate GPS determination. We describe results from simulations and from field testing in open-sky environments where horizontal GPS accuracy was improved from a twice the distance root mean square (2DRMS) error of 5.5 m to 2.99 m for a period of up-to 30 min.

The improvement in integer ambiguity resolution with INS aiding for kinematic precise point positioning

Despite the benefits of integer ambiguity resolution (IAR) in precise point positioning (PPP), observation outages and harsh signal environments still impact float ambiguity estimation in kinematic surveying, consequently resulting in ambiguity-fixed failure. The inertial navigation system (INS) is an autonomous and spontaneous positioning one, which could provide continuous and superior positioning accuracy over short time. Thus, the INS attains more accurate position than code solution. Moreover, the tight integration of INS and PPP is capable of continuous operation where there are less than four satellites available. These advantages can improve float ambiguity estimation and assist in re-initializing the interrupted ambiguity and PPP solution. Based on the good quality of float ambiguity, the ambiguity dilution precision (ADOP) and the size of integer ambiguity search space are reduced, and then, the IAR-PPP is improved. In this work, the INS aiding effect on IAR-PPP was revealed by the sufficient theoretical analysis and performance assessment. A ring laser gyroscope-based navigation-grade IMU and a fiber optic gyroscope-based tactical-grade IMU were utilized to conduct experiments in an open-sky environment and urban area. The assessment adopted the following aspects of ADOP, bootstrapping success rate, time to fix and position errors. It is found that IAR-PPP with INS aiding achieves an enhanced performance during GPS outage when INS could deliver a superior accurate position. For the navigation- and tactical-grade IMU, the INS-aided ambiguity re-fixing performance can be classified as three levels: significant improvement for the outage duration less than 10 s, moderate improvement for the outage duration from 10 to 60 s and a little or zero improvement for the outage duration longer than 60 s. From the viewpoint of the INS-predicted position domain, an accuracy better than 0.1 m and 1.0 m is required for the significant and moderate improvement, while one can only achieve a little or zero improvement if the position error is larger than 1.0 m. Besides, we also performed the INS-aided IAR-PPP in real urban environment. For the urban environments, the span of clean data is often shorter than 30 min due to intermittent signal interruptions; thus, ambiguity re-fixing for PPP always fails. INS-aided information could bridge the data gaps and achieve fast ambiguity re-fixing. In summary, INS aiding information is capable of improving IAR-PPP performance significantly over a short GPS outage.

A Multi-Sensor Positioning Method-Based Train Localization System for Low Density Line

The BeiDou navigation satellite system (BDS) has been widely applied in many areas, including communications, transportation, emergency rescue, public security, surveying, precise positioning, and many other industrial applications. The integration of BDS and an inertial navigation system (INS) has the ability to achieve a more consistent and accurate positioning solution. However, during long BDS outages, the performance of such an integrated system degrades because of the characteristics of the inertial measurement unit—the sensor errors accumulate rapidly with time when operated in a standalone mode. In this paper, a BDS/INS/odometer/map-matching (MM) positioning methodology for train navigation applications is proposed to solve the problem of positioning during BDS outages when trains pass through signal obstructed areas such as under bridges, inside tunnels, and through deep valleys. The seamless transition of the train operation in various scenarios can be, therefore, maintained. When the train operates in an open-sky environment, the BDS signals are available to provide accurate positioning, and the integrated BDS/INS/odometer/MM system is used to correct the INS errors using BDS measurements and to calculate the velocity using the odometer in the navigation frame so as to improve the system reliability and accuracy. In addition, the integrated system also delivers accurate positioning measurements at a high update rate. When the BDS signals are blocked, the integrated system switches to the INS/odometer/MM mode, and the integrated system corrects the INS errors by using odometer measurements. In order to evaluate the proposed system, a real experiment was conducted on the Qinghai–Tibet railway in western China. The experimental results indicate that the proposed system can provide accurate and continuous positioning results in both open-sky and BDS signal-obstructed environments.

A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN).

Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS) will diverge over time dramatically, since there are various and nonlinear errors contained in the MEMS IMU measurements. Therefore, MEMS INS is usually integrated with a Global Positioning System (GPS) for providing reliable navigation solutions. The GPS receiver is able to generate stable and precise position and time information in open sky environment. However, under signal challenging conditions, for instance dense forests, city canyons, or mountain valleys, if the GPS signal is weak and even is blocked, the GPS receiver will fail to output reliable positioning information, and the integration system will fade to an INS standalone system. A number of effects have been devoted to improving the accuracy of INS, and de-nosing or modelling the random errors contained in the MEMS IMU have been demonstrated to be an effective way of improving MEMS INS performance. In this paper, an Artificial Intelligence (AI) method was proposed to de-noise the MEMS IMU output signals, specifically, a popular variant of Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) RNN was employed to filter the MEMS gyroscope outputs, in which the signals were treated as time series. A MEMS IMU (MSI3200, manufactured by MT Microsystems Company, Shijiazhuang, China) was employed to test the proposed method, a 2 min raw gyroscope data with 400 Hz sampling rate was collected and employed in this testing. The results show that the standard deviation (STD) of the gyroscope data decreased by 60.3%, 37%, and 44.6% respectively compared with raw signals, and on the other way, the three-axis attitude errors decreased by 15.8%, 18.3% and 51.3% individually. Further, compared with an Auto Regressive and Moving Average (ARMA) model with fixed parameters, the STD of the three-axis gyroscope outputs decreased by 42.4%, 21.4% and 21.4%, and the attitude errors decreased by 47.6%, 42.3% and 52.0%. The results indicated that the de-noising scheme was effective for improving MEMS INS accuracy, and the proposed LSTM-RNN method was more preferable in this application.

Improved Multipath and NLOS Signals Identification in Urban Environments

GNSS signals in urban environments suffer from reflections and blockage. This causes erroneous measurements and leads to positioning accuracy degradation. Conventional positioning algorithms find the position that minimizes the errors in all the measurements, which results in positioning errors if some measurements are erroneous. This paper proposes an algorithm that identifies and discards erroneous measurements. The algorithm manipulates the measurement equation to express it as a plane in the position estimation errors and performs a search process to find the planes that share an intersection point. Those planes correspond to the error-free measurements, and the shared intersection point corresponds to the estimated position. The algorithm is optimized to reduce the computations. Real GPS and GLONASS data, collected in a multipath urban environment, are used to test the algorithm. The resultant accuracy is 2–3 m, which approaches a standard GNSS receiver accuracy, using legacy GNSS signals and operating in an open-sky environment.

Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone

The signals available for navigation depend on the environment. To operate reliably in a wide range of different environments, a navigation system is required to adopt different techniques based on the environmental contexts. In this paper, an environmental context detection framework is proposed, building the foundation of a context adaptive navigation system. Different land environments are categorized into indoor, urban, and open-sky environments based on how Global Navigation Satellite System (GNSS) positioning performs in these environments. Indoor and outdoor environments are first detected based on the availability and strength of GNSS signals using a hidden Markov model. Then the further classification of outdoor environments into urban and open-sky is investigated. Pseudorange residuals are extracted from raw GNSS measurements in a smartphone and used for classification in a fuzzy inference system alongside the signal strength data. Practical test results under different kinds of environments demonstrate an overall 88.2 percent detection accuracy.

Tracking and Position Errors in GNSS Receivers with Intermittent Signal Tracking

Modern global navigation satellite system (GNSS) receivers in battery-operated devices employ cyclic tracking or intermittent signal tracking to conserve power. The receivers temporarily halt signal tracking during a ‘sleep period’ and continue tracking in the remaining ‘active period.’ The ratio of active period duration to total duration within a position update interval expressed as a percentage is known as the duty cycle. Position accuracy performance equivalent to a continuously tracking receiver is desirable when operating in duty-cycled mode. However, the pseudorange accuracy and, in turn, position accuracy depend on the ability of signal tracking loops to converge faster to thermal noise during the active period. Predicting the code phase and Doppler over receiver sleep period can help this cause. This paper investigates different aspects of intermittent signal tracking in GNSS receivers, characterizes code phase and Doppler prediction errors, points out their significance in intermittent tracking, and analyzes positioning and tracking performance with various duty cycles in different user motion cases in an open sky environment. Copyright # 2016 Institute of Navigation.

Low-Cost BD/MEMS Tightly-Coupled Pedestrian Navigation Algorithm.

Pedestrian Dead Reckoning (PDR) by combining the Inertial Measurement Unit (IMU) and magnetometer is an independent navigation approach based on multiple sensors. Since the inertial component error is significantly determined by the parameters of navigation equations, the navigation precision may deteriorate with time, which is inappropriate for long-time navigation. Although the BeiDou (BD) navigation system can provide high navigation precision in most scenarios, the signal from satellites is easily degraded because of buildings or thick foliage. To solve this problem, a tightly-coupled BD/MEMS (Micro-Electro-Mechanical Systems) integration algorithm is proposed in this paper, and a prototype was built for implementing the integrated system. The extensive experiments prove that the BD/MEMS system performs well in different environments, such as an open sky environment and a playground surrounded by trees and thick foliage. The proposed algorithm is able to provide continuous and reliable positioning service for pedestrian outdoors and thereby has wide practical application.

저가형 수신기를 이용한 GPS/GLONASS/BDS 통합 측위 정확도 분석

본 연구에서는 GPS/GLONASS/BDS 통합 측위를 수행하기 위해 고려해야 할 사항을 소개하였으며, 저가형 수신기를 통해 개활지 환경과 난수신 환경에서의 통합 측위의 정확도를 분석하였다. 개활지 환경에서는 통합 측위 시 수평 RMSE가 1.2m로 단일 시스템만을 이용한 측위에 비해 수평 정확도가 17-55%만큼 향상되었으며 편향이 개선되어 높은 측위 성능을 나타내는 것을 확인하였다. 난수신 환경에서의 가시 위성 개수를 파악한 결과 단일 시스템을 이용하여 측위를 할 때에는 가시 위성의 개수가 4개 미만이 되어 측위가 되지 않는 경우가 발생했으나, 통합 측위를 할 때에는 가시 위성 개수가 항상 4개 이상이 되어 측위가 되지 않는 경우가 발생하지 않았다. 난수신 환경에서 통합 측위의 수평 RMSE는 6.4m로 단일 시스템만을 이용하여 측위를 수행했을 때보다 8-47%만큼 수평 정확도가 향상되는 것을 확인하였다.

Precise Relative Positioning of Vehicles with On-the-Fly Carrier Phase Resolution and Tracking

Forward collision warning systems, lane change assistants, and cooperative adaptive cruise control are examples of safety relevant applications that rely on accurate relative positioning between vehicles. Current solutions estimate the position of an in-front driving vehicle by measuring the distance with a radar sensor, a laser scanner, or a camera system. The perception range of these sensors can be extended by the exchange of GNSS information between the vehicles using an intervehicle communication link. One possibility is to transmit GNSS pseudorange and carrier phase measurements and compute a highly accurate baseline vector that represents the relative position between two vehicles. Solving for the unknown integer ambiguity is specially challenging for low-cost single-frequency receivers. Using the well-known LAMBDA (Least-squares AMBiguity Decorrelation Adjustment) algorithm, in this paper, we present a method for tracking the ambiguity vector solution, which is able to detect and recover from cycle slips and cope with changing satellite constellations. In several test runs performed in real-world open-sky environments with two vehicles, the performance of the proposed Ambiguity Tracker approach is evaluated. The experiments revealed that it is in fact possible to track the position of another vehicle with subcentimeter accuracy over longer periods of time with low-cost single-frequency receivers.