Accuracy In Urban Environments Research Articles

Resilient Pseudorange Error Prediction and Correction for GNSS Positioning in Urban Areas

Positioning, navigation and timing (PNT) is essential for internet of things (IoT) communications and location-based services. Although GNSS can provide accurate PNT in open areas, obtaining reliable PNT is still a considerable technical challenge in complex urban environments. This is because the GNSS signals are more likely to be affected by multipath interference and non-line of sight (NLOS) reception issues arising from the obstructions and reflections in built environments. These introduce range measurement errors that degrade the GNSS positioning accuracy. This paper proposes two resilient pseudorange error prediction and correction strategies to improve the GNSS positioning accuracy in urban environments. In particular, considering the carrier-to-noise density (C/N0), satellite elevation angle and local positional information, the random forest based pseudorange error prediction and correction models are constructed in two variations, including: 1) the point-based correction (PBC), and 2) the grid-based correction (GBC). The final improved positioning solution is then calculated by using the least square method (LSM) of the corrected pseudoranges. Kinematic test results in urban environments show that both variations of the proposed model can improve the positioning accuracy by 42.9% and 40.8% in horizontal, and by 60.1% and 63.3% in 3D, respectively, compared to the positioning results obtained by traditional method without pseudorange error corrections. The improvements are 41.1% and 38.9% in horizontal, and 45.7% and 50.0% in 3D, respectively, compared with traditional elevation angle weighting method.

Carrier Phase Ranging with DTMB Signals for Urban Pedestrian Localization and GNSS Aiding

China developed its Digital Television (DTV) standard in 2006, known as Digital Television Terrestrial Multimedia Broadcasting (DTMB), which employs time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) as the modulation method. In contrast to Global Navigation Satellite Systems (GNSSs), DTV signals have higher transmitting power, wider coverage, larger bandwidth, and fixed transmitter location. This paper explores the positioning performance of DTMB signals, and the potential to improve GNSS positioning accuracy in urban environments. Specifically, a solution is proposed, and a software-defined radio receiver is developed for wireless localization. Without changing the current signal structure, the pseudorandom noise (PN) sequences in the signal are used for signal acquisition and carrier phase ranging. The carrier phase of the first arrived path is extracted by the least squares matching pursuit method. Both static and dynamic field tests were conducted to verify the proposed ranging and positioning method. Centimeter-level ranging accuracy was achieved in the static scenario, while meter-level ranging accuracy was achieved in the dynamic scenario. As one possible application, the proposed ranging method was combined with GPS pseudorange measurements to achieve higher accuracy position results in an urban pedestrian scenario, especially when there is only a limited number of visible satellites.

Improving GNSS/INS Tightly Coupled Positioning by Using BDS-3 Four-Frequency Observations in Urban Environments

Vehicular positioning in urban environments has become a research hotspot in recent years, and global navigation satellite system/inertial navigation system (GNSS/INS) tightly coupled positioning is a commonly used method. With the broadcast of BDS-3 and Galileo four-frequency signals, the multi-frequency and multi-system tightly coupled positioning method provides more possibilities for vehicular positioning in urban environments. At present, most of the studies on multi-frequency and multi-system mainly focus on static or baseline solutions, and there are few studies on the urban dynamic environment. In this paper, based on the triple-frequency GPS/BDS-2/INS tightly coupled positioning model, the BDS-3 four-frequency observation is introduced to conduct a preliminary study on the performance of GPS/BDS-2/BDS-3/INS tightly coupled positioning. Taking into account the positioning accuracy and calculation efficiency of the tightly coupled positioning, single epoch extra-wide-lane/wide-lane (EWL/WL) observation is used to participate in the modeling of tightly coupled positioning. The EWL/WL ambiguity is solved by the geometry-free (GF) method, in which triple-frequency carrier ambiguity resolution (TCAR) and four-frequency carrier ambiguity resolution (FCAR) are used for triple-frequency and four-frequency observations, respectively. Finally, the positioning performance of the tightly coupled method in this paper is evaluated through vehicular experiment. The experimental results show that in the urban dynamic environment, due to the higher quality of the linear combination of BDS-3 four-frequency, the positioning accuracy of the BDS-3/INS tightly coupled was slightly better than that of the triple-frequency BDS-2/INS. Compared with GPS/BDS-2/INS, the GPS/BDS-2/BDS-3/INS tightly coupled positioning accuracy increased by 29.1% and 58.7% in horizontal and vertical directions, respectively, which can realize decimeter positioning accuracy in urban environments.

A new method for evaluation of the positional error of low-cost devices based on GNSS integrity for transportation applications

GNSS integrity assessment has always been linked to the need for reliable positional information. Initially used in aviation, positional information gained even more relevance in terrestrial applications with the popularity of GNSS. However, the terrestrial environment has many influences over GNSS signals, which reduces the positional quality of tracking objects. Advances have been achieved in the use of integrity monitoring algorithms, but there are limitations to their use, especially those concerning positional accuracy in urban environments with low-cost devices. This paper aims to discuss a comparative method using two low-cost GNSS receivers designed for transportation applications and to verify whether this method can evaluate positional quality in pre-established locations, as well as the possibilities of using these devices for transportation applications, considering the positional error. Results show that, in the static experiment, the receiver assembled with a GPS antenna active embedded was more accurate than the receiver assembled with an external antenna, presenting better values in 5 out of 10 evaluated sites, while the external antenna performed better in only 2 sites. However, in a kinematic evaluation, the receiver assembled with an external antenna provided better results when considering positional error as assessment criterion, resulting in values less than or equal to 8 meters in 99.7% of the route evaluated, while the embedded antenna had 95.3%.

Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection.

Correct ego-lane index estimation is essential for lane change and decision making for intelligent vehicles, especially in global navigation satellite system (GNSS)-challenged environments. To achieve this, we propose an ego-lane index estimation approach in an urban scenario based on particle filter (PF). The particles are initialized and propagated by dead reckoning with inertial measurement unit (IMU) and odometry. A lane-level map is used to navigate the particles taking advantage of topologic and geometric information of lanes. GNSS single-point positioning (SPP) can provide position estimation with meter-level accuracy in urban environments, which can limit drift introduced by dead reckoning for updating the weight of each particle. Light detection and ranging (LiDAR) is a common sensor in an intelligent vehicle. A LiDAR-based road boundary detection method provides distance measurements from the vehicle to the left/right road boundaries, which provides a measurement for importance weighting. However, the high precision of the LiDAR measurements may put a tight constraint on the distribution of particles, which can lead to particle degeneration with sparse particle sets. To deal with this problem, we propose a novel step that shifts particles laterally based on LiDAR measurements instead of importance weighting in the traditional PF scheme. We tested our methods on an urban expressway at a low traffic volume period to ensure road boundaries can be detected by LiDAR measurements at most time steps. Experimental results prove that our improved PF scheme can correctly estimate ego-lane index at all time steps, while the traditional PF scheme produces wrong estimations at some time steps.

An SVM Based Weight Scheme for Improving Kinematic GNSS Positioning Accuracy with Low-Cost GNSS Receiver in Urban Environments.

High-precision positioning with low-cost global navigation satellite systems (GNSS) in urban environments remains a significant challenge due to the significant multipath effects, non-line-of-sight (NLOS) errors, as well as poor satellite visibility and geometry. A GNSS system is typically implemented with a least-square (LS) or a Kalman-filter (KF) estimator, and a proper weight scheme is vital for achieving reliable navigation solutions. The traditional weight schemes are based on the signal-in-space ranging errors (SISRE), elevation and C/N0 values, which would be less effective in urban environments since the observation quality cannot be fully manifested by those values. In this paper, we propose a new multi-feature support vector machine (SVM) signal classifier-based weight scheme for GNSS measurements to improve the kinematic GNSS positioning accuracy in urban environments. The proposed new weight scheme is based on the identification of important features in GNSS data in urban environments and intelligent classification of line-of-sight (LOS) and NLOS signals. To validate the performance of the newly proposed weight scheme, we have implemented it into a real-time single-frequency precise point positioning (SFPPP) system. The dynamic vehicle-based tests with a low-cost single-frequency u-blox M8T GNSS receiver demonstrate that the positioning accuracy using the new weight scheme outperforms the traditional C/N0 based weight model by 65.4% and 85.0% in the horizontal and up direction, and most position error spikes at overcrossing and short tunnels can be eliminated by the new weight scheme compared to the traditional method. It also surpasses the built-in satellite-based augmentation systems (SBAS) solutions of the u-blox M8T and is even better than the built-in real-time-kinematic (RTK) solutions of multi-frequency receivers like the u-blox F9P and Trimble BD982.

Improving GPS Code Phase Positioning Accuracy in Urban Environments Using Machine Learning

The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. These effects cause range errors that degrade GPS positioning accuracy. Enhancements in the design of antennae and receivers deliver a level of reduction of multipath. However, NLOS signal reception and residual effects of multipath are still to be mitigated sufficiently for improvements in range errors and positioning accuracy. Recent machine learning-based methods have shown promise in improving pseudorange-based position solutions by considering multiple variables from raw GPS measurements. However, positioning accuracy is limited by low accuracy signal reception classification. Unlike the existing methods, which use machine learning to directly predict the signal reception classification, we use a gradient boosting decision tree (GBDT)-based method to predict the pseudorange errors by considering the signal strength, satellite elevation angle and pseudorange residuals. With the predicted pseudorange errors, two variations of the algorithm are proposed to improve positioning accuracy. The first corrects pseudorange errors and the other either corrects or excludes the signals determined to contain the effects of multipath and NLOS signals. The results for a challenging urban environment characterized by high-rise buildings on one side, show that the 3-D positioning accuracy of the pseudorange error correction-based positioning measured in terms of the root mean square error is 23.3 m, an improvement of more than 70% over the conventional methods.

GPS multipath and NLOS mitigation for relative positioning in urban environments

Multipath interference and non-line-of-sight (NLOS) reception are major error sources when using global navigation satellite system-based cooperative positioning (CP) in urban environments. In this paper, we develop a multipath and NLOS mitigation algorithm that significantly improves the relative positioning accuracy in urban environments, compared with the absolute positioning and conventional tight CP technique, respectively. In this proposed method, each receiver fuses the Global Positioning System (GPS) pseudorange and Doppler shift measurements, and the linear cooperative navigation observation model is formulated by considering the multipath and NLOS biases as additive sparse errors. We investigate a sparse estimation algorithm to solve the navigation problem and estimate the multipath and NLOS biases, with the weighting matrix designed as function of satellite carrier-to-noise density ratio and the regularization parameter selected by Bayesian information criterion. Based on a least absolute shrinkage and selection operator problem, an ℓ1 regularization is introduced to enforce sparsity in the biases estimation model, which can be solved by the reweighted-ℓ1 algorithm. The GPS measurements can be corrected by subtracting the estimated multipath and NLOS biases, and the relative positioning performance of proposed method is verified by analytical and experimental results.

Multipath modeling and mitigation by using sparse estimation in global navigation satellite system-challenged urban vehicular environments

Multipath interference has been one of the most difficult problems when using global navigation satellite system-based vehicular navigation in urban environments. In this article, we develop a multipath mitigation algorithm exploiting the sparse estimation theory that improves the absolute positioning accuracy in urban environments. The navigation observation model is established by considering the multipath bias as additive positioning errors, and the assumption for the proposed method is that global navigation satellite system signals contaminated due to multipath are the minority among the received signals, which makes the unknown bias vector sparse. We investigated an improved elastic net method to estimate the sparse multipath bias vector, and the global navigation satellite system measurements can be corrected by subtracting the estimated multipath error. The positioning performance of the proposed method is verified by analytical and experimental results.

Improved urban navigation with shadow matching and specular matching

As the dependence of Global Navigation Systems (GNSS) increases, so does a growing demand for GNSS accuracy in urban environments. This research aims to improve navigation in these environments by integrating non-line-of-sight signals, building models, and measured signal to noise ratios in ways not typically used in GNSS positioning. We propose a technique of combining elements of shadow matching, non-line-of-sight signal prediction through ray tracing, and collaborative navigation. A specularity metric is developed, which predicts the likelihood of building reflections resulting in non-line-of-sight signal reception, and is used in conjunction with shadow matching techniques to improve positioning. A framework for implementing these approaches is presented and demonstrated using improved positioning techniques built and tested using real-world data collected in urban surroundings.

OPTIMIZING OBJECT-BASED CLASSIFICATION IN URBAN ENVIRONMENTS USING VERY HIGH RESOLUTION GEOEYE-1 IMAGERY

Abstract. The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In fact, one of the most common applications of remote sensing images is the extraction of land cover information for digital image base maps by means of classification techniques. When VHR satellite images are used, an object-based classification strategy can potentially improve classification accuracy compared to pixel based classification. The aim of this work is to carry out an accuracy assessment test on the classification accuracy in urban environments using pansharpened and panchromatic GeoEye-1 orthoimages. In this work, the influence on object-based supervised classification accuracy is evaluated with regard to the sets of image object (IO) features used for classification of the land cover classes selected. For the classification phase the nearest neighbour classifier and the eCognition v. 8 software were used, using seven sets of IO features, including texture, geometry and the principal layer values features. The IOs were attained by eCognition using a multiresolution segmentation approach that is a bottom-up regionmerging technique starting with one-pixel. Four different sets or repetitions of training samples, always representing a 10% for each classes were extracted from IOs while the remaining objects were used for accuracy validation. A statistical test was carried out in order to strengthen the conclusions. An overall accuracy of 79.4% was attained with the panchromatic, red, blue, green and near infrared (NIR) bands from the panchromatic and pansharpened orthoimages, the brightness computed for the red, blue, green and infrared bands, the Maximum Difference, a mean of soil-adjusted vegetation index (SAVI), and, finally the normalized Digital Surface Model or Object Model (nDSM), computed from LiDAR data. For buildings classification, nDSM was the most important feature attaining producer and user accuracies of around 95%. On the other hand, for the class "vegetation", SAVI was the most significant feature, obtaining accuracies close to 90%.

A Novel Approach for Modeling Land Vehicle Kinematics to Improve GPS Performance Under Urban Environment Conditions

The satellite blockage problem, which adversely affects Global Positioning System (GPS) accuracy in urban environments, is addressed in this work. To provide a position solution, there must be at least four satellites within line of sight (LOS) of the receiver (vehicle). However, when satellite blockage occurs, this requirement is not met because most of the sky is obscured by tall buildings, and only a narrow sky sector is exposed to the receiver. Given the short duration of satellite blockages, an efficient solution to this problem can be accomplished through reliable modeling of vehicle motion. In this manner, information regarding vehicle motion can be more precisely obtained in the absence of a sufficient number of measurements. In this paper, a model that uses intrinsic quantities as part of the description of the vehicle path, such as curvature, tangent angle, and tangential speed, is proposed to achieve a more accurate modeling of both the trajectory and kinematics of a land vehicle. The model, which was implemented via an extended Kalman filter, was examined against currently known models during satellite blockage scenarios and demonstrated superior performance.

Shannon Capacity Bound on Mobile Station Localization Accuracy in Urban Environments

The Federal Communications Commission (FCC) Phase II enhanced 911 (E-911) requirements have motivated increased interest in the challenge of localizing mobile sources in urban environments. The FCC E-911 Phase-II requires wireless carriers to provide the location of a 911 caller with accuracy of 100 meters in 67% of cases and 300 meters in 95% of cases. This work proposes an information theoretic bound on mobile station (MS) localization performance in urban environments that provides an alternative criterion for the MS localization performance evaluation. The proposed lower bound provides necessary, but not sufficient conditions for achieving arbitrary small probability of the localization error. The MS localization problem is modeled in the framework of communication via channels where the source location is modeled as a transmitted message which is encoded by the propagation conditions. The bound is evaluated for variety of propagation conditions predicted by a recently proposed statistical model of urban propagation conditions. The bound is demonstrated to apply to urban scenes of interest, and the sensitivity of the bound to changes in propagation conditions is discussed.

Enhanced Urban Path Loss Prediction Model with New Correction Factors

SUMMARY Modification of ITU-R P.1411 model to enhance the prediction accuracy in urban environments having variable heights of buildings is proposed in this paper by introducing two kinds of novel correction factors. One is considering the relationship of the highest building height and the transmitter (Tx) antenna height, and the other is considering the effect of receiver (Rx) position on crossroads. After introducing two correction