Reliable Positioning Results Research Articles

An Effective GNSS/PDR Fusion Positioning Algorithm on Smartphones for Challenging Scenarios.

The location-based smartphone service brings new development opportunities for seamless indoor/outdoor positioning. However, in complex scenarios such as cities, tunnels, overpasses, forests, etc., using only GNSS on smartphones cannot provide stable and reliable positioning results. Usually, additional sensors are needed to assist GNSS. This paper investigates the GNSS positioning algorithm assisted by pedestrian dead reckoning (PDR) in complex scenarios. First, we introduce a step detection algorithm based on the peak-valley of acceleration modulus, and the Weinberg model and the Mahony algorithm in PDR are used to estimate step length and heading. On this basis, we evaluated the performance of GNSS/PDR fusion positioning in an open scenario, a semiopen scenario, and a blocked scenario, respectively. Finally, we develop a GNSS/PDR real-time positioning software, called China University of Mining and Technology-POSitioning (CUMT-POS) version 1.0, on the Android 10 platform. By comparing GNSS solutions, PDR solutions, GNSS/PDR solutions, and real-time kinematic (RTK) solutions, we verify the potential auxiliary ability of PDR for GNSS positioning in complex environments, proving that multisource sensor fusion positioning significantly improves reliability and stability. Our research can help the realization of urban informatization and smart cities.

Stochastic modeling with robust Kalman filter for real-time kinematic GPS single-frequency positioning

The centimeter-level positioning accuracy of real-time kinematic (RTK) depends on correctly resolving integer carrier-phase ambiguities. To improve the success rate of ambiguity resolution and obtain reliable positioning results, an enhanced Kalman filtering procedure has been developed. Based on a posteriori residuals of measurements and state predictions, the measurement noise variance–covariance matrix for double-differenced measurements is adaptively estimated, rather than approximated by an empirical function which uses satellite elevation angle as input. Since, in real-world situations, unexpected outliers and carrier-phase outages can degrade the filter performance, a stochastic model based on robust Kalman filtering is proposed, for which the double-differenced measurement noise variance–covariance matrix is computed empirically with a modified version of the IGG (Institute of Geodesy and Geophysics) III method in order to detect and identify outliers. The performance of the proposed method is assessed by two tests, one with simulated data and one with real data. In addition, the performance of F-ratio and W-ratio tests as proxies for the success of ambiguity fixing is investigated. Experimental results reveal that the proposed method can improve the reliability and robustness of relative kinematic positioning for simulation scenarios as well as in a real urban test.

A Novel Deep Learning Approach to 5G CSI/Geomagnetism/VIO Fused Indoor Localization.

For positioning tasks of mobile robots in indoor environments, the emerging positioning technique based on visual inertial odometry (VIO) is heavily influenced by light and suffers from cumulative errors, which cannot meet the requirements of long-term navigation and positioning. In contrast, positioning techniques that rely on indoor signal sources such as 5G and geomagnetism can provide drift-free global positioning results, but their overall positioning accuracy is low. In order to obtain higher precision and more reliable positioning, this paper proposes a fused 5G/geomagnetism/VIO indoor localization method. Firstly, the error back propagation neural network (BPNN) model is used to fuse 5G and geomagnetic signals to obtain more reliable global positioning results; secondly, the conversion relationship from VIO local positioning results to the global coordinate system is established through the least squares principle; and finally, a fused 5G/geomagnetism/VIO localization system based on the error state extended Kalman filter (ES-EKF) is constructed. The experimental results show that the 5G/geomagnetism fusion localization method overcomes the problem of low accuracy of single sensor localization and can provide more accurate global localization results. Additionally, after fusing the local and global positioning results, the average positioning error of the mobile robot in the two scenarios is 0.61 m and 0.72 m. Compared with the VINS-mono algorithm, our approach improves the average positioning accuracy in indoor environments by 69.0% and 67.2%, respectively.

Carrier Assisted Robust Unambiguous Positioning Technique for Subcarrier Modulated Signals in GNSS

Various subcarrier modulations bring wider Gabor bandwidths and higher-precision ranging and positioning potential to the new generation of Global Navigation Satellite System (GNSS) signals. However, they may also introduce intolerable ambiguity problems for signal tracking, ranging, and positioning. In order to take advantage of high-precision subcarriers in subcarrier modulated signals for unambiguous ranging and positioning, we propose a Carrier Assisted Robust Unambiguous Positioning Technique (CARUPT), which consists of a Carrier Assisted Kalman filter (CA-Kalman) module and an Observation Cycle-slip Detection (OCD) module. The CA-Kalman module utilizes the undifferenced code and subcarrier observations and time-differenced carrier observations derived from two-dimensional tracking to resolve subcarrier ambiguities in the position domain. The OCD module includes a carrier-involved Subcarrier Cycle-slip Detector (SCD) and a high-order difference-based Carrier Cycle-slip Detector (CCD) so as to avoid the impact of subcarrier and carrier cycle slips on the robust resolution of subcarrier ambiguities. In this paper, performances in theory, simulation, and real data experiments comprehensively demonstrate that compared with existing techniques, the CARUPT can resolve subcarrier ambiguities with faster convergence speed, higher accuracy, and robustness, deriving the most reliable unambiguous positioning results.

An Improved Method Based on Bluetooth Low-Energy Fingerprinting for the Implementation of PEPS System.

In the automotive field, the introduction of keyless access systems is revolutionizing car entry techniques currently dominated by a physical key. In this context, this paper investigates the possible use of smartphones to create a PEPS (Passive Entry Passive Start) system using the BLE (Bluetooth Low-Energy) Fingerprinting technique that allows, along with a connection to a low-cost BLE micro-controllers network, determining the driver's position, either inside or outside the vehicle. Several issues have been taken into account to assure the reliability of the proposal; in particular, (i) spatial orientation of each microcontroller-based BLE node which ensures the best performance at 180° and 90° referred to as the BLE scanner and the advertiser, respectively; (ii) data filtering techniques based on Kalman Filter; and (iii) definition of new network topology, resulting from the merger of two standard network topologies. Particular attention has been paid to the selection of the appropriate measurement method capable of assuring the most reliable positioning results by means of the adoption of only six embedded BLE devices. This way, the global accuracy of the system reaches 98.5%, while minimum and maximum accuracy values relative to the individual zones equal, respectively, to 97.3% and 99.4% have been observed, thus confirming the capability of the proposed method of recognizing whether the driver is inside or outside the vehicle.

A Framework for Low-cost Fusion Positioning with Single Frequency RTK/MEMS-IMU/VIO

In response to the demand for high-precision positioning in the consumer market for low-speed UAVs, this paper proposes an ultra-low-cost fusion positioning system architecture, selecting consumer-grade GNSS Ublox M8T, IMU MPU 9250 modules and binocular cameras as the main equipment. We fused raw data measured from multi-sensors by expansion Kalman filter(EKF), and tested the fusion positioning performance of MEMS-IMU and VIO through simulation interruption experiment. The experimental results show that the ultra-low-cost single-frequency RTK/VIO/MEMS-IMU multi-sensor fusion positioning system can provide high-precision, reliable, continuous and smooth positioning results.

A method of improving ambiguity fixing rate for post-processing kinematic GNSS data

Global Navigation Satellite System precise positioning using carrier phase measurements requires reliable ambiguity resolution. It is challenging to obtain continuous precise positions with a high ambiguity fixing rate under a wide range of dynamic scenes with a single base station, thus the positioning accuracy will be degraded seriously. The Forward–Backward Combination (FBC), a common post-processing smoothing method, is simply the weighted average of the positions of forward and backward filtering. When the ambiguity fixing rate of the one-way (forward or backward) filter is low, the FBC method usually cannot provide accurate and reliable positioning results. Consequently, this paper proposed a method to improve the accuracy of positions by integrating forward and backward AR, which combines the forward and backward ambiguities instead of positions—referred to as ambiguity domain-based integration (ADBI). The purpose of ADBI is to find a reliable correct integer ambiguities by making full use of the integer nature of ambiguities and integrating the ambiguities from the forward and backward filters. Once the integer ambiguities are determined correctly and reliably with ADBI, then the positions are updated with the fixing ambiguities constrained, in which more accurate positions with high confidence can be achieved. The effectiveness of the proposed approach is validated with airborne and car-borne dynamic experiments. The experimental results demonstrated that much better accuracy of position and higher ambiguity-fixed success rate can be achieved than the traditional post-processing method.

MIMU/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage: Testing and Results.

With the rapid development of autonomous vehicles, the demand for reliable positioning results is urgent. Currently, the ground vehicles heavily depend on the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) providing reliable and continuous navigation solutions. In dense urban areas, especially narrow streets with tall buildings, the GNSS signals are possibly blocked by the surrounding tall buildings, and under this condition, the geometry distribution of the in-view satellites is very poor, and the None-Line-Of-Sight (NLOS) and Multipath (MP) heavily affects the positioning accuracy. Further, the INS positioning errors will quickly diverge over time without the GNSS correction. Aiming at improving the position accuracy under signal challenging environment, in this paper, we developed an MIMU(Micro Inertial Measurement Unit)/Odometer integration system with vehicle state constraints (MO-C) for improving the vehicle positioning accuracy without GNSS. MIMU/Odometer integration model and the constrained measurements are given in detail. Several field tests were carried out for evaluating and assessing the MO-C system. The experiments were divided into two parts, firstly, field testing with data post-processing and real-time processing was carried out for fully assessing the performance of the MO-C system. Secondly, the MO-C was implemented in the BeiDou Satellite Navigation System (BDS)/integrated navigation system (INS) for evaluating the MO-C performance during the BDS signal outage. The MIMU standalone positioning accuracy was compared with that from the MIMU/Odometer integration (MO), MO-C and MIMU with constraints (M-C) for assessing the Odometer, and the influence of the constraint on the positioning errors reduction. The results showed that the latitude and longitude errors could be suppressed with Odometer assisting, and the height errors were suppressed while the state constraints were included.

SNR-Dependent Environmental Model: Application in Real-Time GNSS Landslide Monitoring.

The Global Navigation Satellite System (GNSS) is currently one of the important tools for landslide monitoring and early warning. However, the majority of GNSS devices are installed in mountainous areas and a variety of vegetation. These harsh environments lead to defective signals at high elevation angles, rendering real-time successive and reliable positioning results for monitoring difficult. In this study, an environmental model derived from signal-to-noise ratio (SNR) is proposed to enhance the precision and convergence time of positioning in harsh environments. A series of experiments are conducted on weighting and ambiguity-fixed models to evaluate performance. The results indicate that the proposed SNR-dependent environment model could lead to a significant improvement in precision and convergence time; with an obtained root mean squared result on the millimeter level, a convergence time of a few seconds, and utilization which could reach 100%, for continuous and reliable positioning results. These results indicate that the proposed SNR-dependent environment model enhances the performance of GNSS monitoring and early warning to provide continuous and reliable positioning results in real-time.

Influence of stochastic modeling for inter-system biases on multi-GNSS undifferenced and uncombined precise point positioning

The focus of this study is on proper modeling of the dynamics for inter-system biases (ISBs) in multi-constellation Global Navigation Satellite System (GNSS) precise point positioning (PPP) processing. First, the theoretical derivation demonstrates that the ISBs originate from not only the receiver-dependent hardware delay differences among different GNSSs but also the receiver-independent time differences caused by the different clock datum constraints among different GNSS satellite clock products. Afterward, a comprehensive evaluation of the influence of ISB stochastic modeling on undifferenced and uncombined PPP performance is conducted, i.e., random constant, random walk process, and white noise process are considered. We use data based on a 1-month period (September 2017) Multi-GNSS Experiment (MGEX) precise orbit and clock products from four analysis centers (CODE, GFZ, CNES, and WHU) and 160 MGEX tracking stations. The results demonstrate that generally, the positioning performance of PPP in terms of convergence time and positioning accuracy with the final products from CODE, CNES, and WHU is comparable among the three ISB handling schemes. However, estimating ISBs as random walk process or white noise process outperforms that as the random constant when using the GFZ products. These results indicate that the traditional estimation of ISBs as the random constant may not always be reasonable in multi-GNSS PPP processing. To achieve more reliable positioning results, it is highly recommended to consider the ISBs as random walk process or white noise process in multi-GNSS PPP processing.

Image Mosaicing Using Inertial Navigation System for Increasing Match Information in Scene Matching Navigation System

Scene matching is one of the key techniques to revise the positioning accumulation error of Inertia Navigation System (INS) for many kinds of unmanned air vehicles (UAVs). When the altitude of aircraft is too low or the ground texture is not rich enough, the traditional scene matching method might fail to get reliable positioning results because of lack of match information. Based on those considerations, we present a new synchronous mosaicing and matching technique based on image mosaicing using INS information to increase match information for scene matching navigation system. In order to achieve image mosaicing, the attitude information measured by INS at imaging instant are used to geometrically transform real-time images to a unified reference coordinate system. Using this scheme, we can eliminate the geometric distortion between real-time images, and then achieve image mosaicing accurately and reliably. In order to ensure the real-time performance of the system, we present a new synchronous mosaicing and matching technique (SMMT). Because of the increase of match information, our method can get more reliable positioning results, and also can reduce the requirement for image texture. Scene matching using many real-time images obtained from the electro-optic pod system clearly justify our method.

Design of an enhanced visual odometry by building and matching compressive panoramic landmarks online

Efficient and precise localization is a prerequisite for the intelligent navigation of mobile robots. Traditional visual localization systems, such as visual odometry (VO) and simultaneous localization and mapping (SLAM), suffer from two shortcomings: a drift problem caused by accumulated localization error, and erroneous motion estimation due to illumination variation and moving objects. In this paper, we propose an enhanced VO by introducing a panoramic camera into the traditional stereo-only VO system. Benefiting from the 360° field of view, the panoramic camera is responsible for three tasks: (1) detecting road junctions and building a landmark library online; (2) correcting the robot’s position when the landmarks are revisited with any orientation; (3) working as a panoramic compass when the stereo VO cannot provide reliable positioning results. To use the large-sized panoramic images efficiently, the concept of compressed sensing is introduced into the solution and an adaptive compressive feature is presented. Combined with our previous two-stage local binocular bundle adjustment (TLBBA) stereo VO, the new system can obtain reliable positioning results in quasi-real time. Experimental results of challenging long-range tests show that our enhanced VO is much more accurate and robust than the traditional VO, thanks to the compressive panoramic landmarks built online.

MineLoc — Personnel Tracking System for Longwall Coal Mining Sites

The localization of people and objects in indoor environments is an ongoing research challenge with a diverse set of possible positioning sensors. For the harsh environment of an underground coal mine, a system relying on received signal strength (RSS) measurements of common radio modules has been proven to deliver accurate and reliable positioning results. However, in tunnel environments of a longwall mining facility, conventional indoor radio propagation models are not valid due to waveguide effects. Additional investigations regarding the RSS-based ranging and the localization algorithm will be proposed. Ray tracing is used to find an appropriate propagation model and to define suitable range sections for different radio frequency bands. Simulation and experiment ranging results are compared for 2.4 GHz and sub- GHz radios. An enhanced weighted centroid localization (WCL) approach is essential, taking into account the peculiarities of the longwall mining environment.

An Extended Dynamic Model for Kinematic Positioning

The main problems faced by a dynamic model within a Kalman filter occur when the system experiences unexpected dynamic conditions, a change in data acquisition rate, or when the dynamics of the system are non-linear. To minimize the errors produced from dynamic modelling in unusual conditions, an extended dynamic model is developed in this paper, and its usefulness demonstrated through comparison of the performance of a Kalman filter's response to simulated data with a standard dynamic model and the extended dynamic model. The results show that, in use, the proposed extended dynamic model is superior to a standard dynamic model, due mainly to its ability to adapt to a wider range of dynamic conditions, which in turn ensures the optimization of the Kalman filter and the consequent generation of reliable positioning results.

On-line detection of abrupt changes in the carrier-phase measurements of GPS

The detection of cycle slips in carrier-phase data, outliers in phase rate or in code ranges, or any other type of disorder in the measurements of the GPS system, is one of the major quality-control problems that needs to be addressed, especially where GPS is used for real-time applications or for those applications that require continuous and reliable positioning results. This study is concerned with designing algorithms to detect failures or changes of small magnitude, in order to monitor and control the quality of GPS measurements for critical real-time and/or deformation applications. In this regard, the statistical technique known as the cumulative-sum test is described for the detection of abrupt changes in the GPS carrier-phase measurements. Real GPS data have been used to test and evaluate the algorithm.