Cooperative Positioning System Research Articles

A Dual UAV Cooperative Positioning System With Advanced Target Detection and Localization

A Dual UAV Cooperative Positioning System With Advanced Target Detection and Localization

Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects.

Automation of transportation will play a crucial role in the future when people driving vehicles will be replaced by autonomous systems. Currently, the positioning systems are not used alone but are combined in order to create cooperative positioning systems. The ultra-wideband (UWB) system is an excellent alternative to the global positioning system (GPS) in a limited area but has some drawbacks. Despite many advantages of various object positioning systems, none is free from the problem of object displacement during measurement (data acquisition), which affects positioning accuracy. In addition, temporarily missing data from the absolute positioning system can lead to dangerous situations. Moreover, data pre-processing is unavoidable and takes some time, affecting additionally the object's displacement in relation to its previous position and its starting point of the new positioning process. So, the prediction of the position of an object is necessary to minimize the time when the position is unknown or out of date, especially when the object is moving at high speed and the position update rate is low. This article proposes using the long short-term memory (LSTM) artificial neural network to predict objects' positions based on historical data from the UWB system and inertial navigation. The proposed solution creates a reliable positioning system that predicts 10 positions of low and high-speed moving objects with an error below 10 cm. Position prediction allows detection of possible collisions-the intersection of the trajectories of moving objects.

Fault-Tolerant Cooperative Positioning Based on Hybrid Robust Gaussian Belief Propagation

This paper proposes a hybrid robust Gaussian belief propagation (HRGBP) as a fault-tolerant cooperative positioning (CP) system that can be used to support cooperative intelligent transportation applications. For fault-tolerant state estimation, it is well known that fault detection and exclusion (FDE) based methods and Huber’s M-estimation based methods have their own drawbacks when facing different forms of observation outliers, or faults. To solve this problem, our proposed HRGBP uses an interactive multiple model (IMM) framework to fuse these two strategies, which combines the advantages of both methods without their drawbacks. HRGBP can fully exploit the message passing process to mitigate the biased estimates, which further improves the system’s fault-tolerant robustness. HRGBP can be further adapted to fuse more fault-tolerant strategies to improve the robustness of the CP system, and be extended to other factor graph-based methods. Here, we evaluate HRGBP for observations from visual landmark range and bearing, neighboring vehicle range and bearing, and odometer. Our evaluations show that HRGBP outperforms other state-of-the-art CP methods.

Vehicular Relative Positioning With Measurement Outliers and GNSS Outages

Relative positioning among vehicles finds many applications regarding location-based services in intelligent transportation systems (ITSs). This article introduces a hybrid relative positioning strategy integrating global navigation satellite system (GNSS), inertial navigation system (INS), and ultrawideband (UWB) observations, which addresses measurement outliers and GNSS outages simultaneously. An improved extended Kalman filter (IEKF) based on tracking an appropriately weighted window of past innovations is developed to deal with measurement outliers. To perform relative positioning during GNSS outages, a nonlinear autoregressive network with exogenous inputs (NARX) is developed to predict GNSS measurements increments, with the least-squares support vector machine (LSSVM) algorithm identifying the NARX model. The NARX-LSSVM module learns the relationship between GNSS pseudorange and Doppler shift increments of a target vehicle and its local and neighbors’ dynamics. Whereas during GNSS outages, the increments of GNSS measurements are generated by this prediction algorithm. The feasibility of the proposed methodology is evaluated on empirical road data, and improved positioning accuracy is achieved in cooperative positioning (CP) systems.

A Novel GNSS Fault Detection and Exclusion Method for Cooperative Positioning System

Global Navigation Satellite System (GNSS)-based cooperative positioning is highly degraded in dense urban areas due to non-line-of-sight (NLOS) and multipath effects. Multiple simultaneous faulty GNSS measurements can result in traditional fault detection and exclusion (FDE) methods finding a consistent but erroneous group of satellites. This paper describes a novel distributed FDE method based on cooperative reliability message (CRM), which is suitable for cooperative positioning system in connected vehicles. The idea is to take into account not only the consistency check on raw GNSS measurements, but also the prior knowledge about the quality of GNSS measurements offered by nearby cooperators so as to improve the ability to exclude multiple GNSS faults. The line-of-sight (LOS) satellites identified by cooperators are stored in CRMs, which will be shared to the host vehicle to generate the candidates for the set of LOS satellites observed locally. Based on the candidates, a bottom-up searching strategy is designed to find the healthy satellites and isolate the faulty ones precisely. Several experiments involving four moving vehicles were conducted in dense urban areas. Results indicate the superiority of CRM-based method in excluding multiple faults over existing methods.

Decision Tree based diagnosis for hybrid model-based/data-driven fault detection and exclusion of a decentralized multi-vehicle cooperative localization system*

Cooperative navigation systems are one of the main topics of interest in multi-robot systems emerging nowadays, where the question of safety remains a very critical one preventing the actual integration of the technology. In this article, a multi-sensor multi-vehicle Cooperative Positioning System (CPS) is presented, with a hybrid fault detection method under a decentralized architecture, and that is tolerant to simultaneous sensor faults. In order to detect and isolate faults, a set of fault sensitive residuals are generated based on the divergence of Jensen Shannon (DJS) between the probability distributions predicted by the encoder based evolution model and the various observations obtained by sensors. Then, in order to detect a fault, a data-driven approach is applied, where the classification of faults is done by a pre-trained detection decision tree (D-DT) and isolation random forest (I-RF). The testing and evaluation of the approach is done on real data from three Turtlebot3 burger robot equipped with wheel encoders (for prediction), a gyroscope (for the yaw angle) and a Marvelmind (for the absolute position). A ground truth is also recorded using optitrack system.

Consistent Extended Kalman Filter-Based Cooperative Localization of Multiple Autonomous Underwater Vehicles.

In order to solve the problem of inconsistent state estimation when multiple autonomous underwater vehicles (AUVs) are co-located, this paper proposes a method of multi-AUV co-location based on the consistent extended Kalman filter (EKF). Firstly, the dynamic model of cooperative positioning system follower AUV under two leaders alternately transmitting navigation information is established. Secondly, the observability of the standard linearization estimator based on the lead-follower multi-AUV cooperative positioning system is analyzed by comparing the subspace of the observable matrix of state estimation with that of an ideal observable matrix, it can be concluded that the estimation of state by standard EKF is inconsistent. Finally, aiming at the problem of inconsistent state estimation, a consistent EKF multi-AUV cooperative localization algorithm is designed. The algorithm corrects the linearized measurement values in the Jacobian matrix for cooperative positioning, ensuring that the linearized estimator can obtain accurate measurement values. The positioning results of the follower AUV under dead reckoning, standard EKF, and consistent EKF algorithms are simulated, analyzed, and compared with the real trajectory of the following AUV. The simulation results show that the follower AUV with a consistent EKF algorithm can keep synchronization with the leader AUV more stably.

ANALYSIS OF THE PERFORMANCE OF AN UWB-BASED COOPERATIVE POSITIONING FOR DIFFERENT CAR PLATOON CONFIGURATIONS

Abstract. The increasing interest in autonomous vehicles motivates the researches aiming at developing reliable positioning system also in conditions challenging for the Global Navigation Satellite Systems (GNSS), such as in urban canyons, tunnels, under quite dense vegetation. The uso of Ultra Wide-Band (UWB) systems is among the quite well known methods for providing reasonable positioning results without exploiting GNSS. UWB systems are typically used indoors, however their use can be of interest also outdoors, in particular when the need is to ensure good positioning results over a quite small area. This paper investigates the use of UWB systems for positioning in the case of terrestrial vehicles, and, more specifically, it focuses on checking the influence of car platoon configurations on the performance of an UWB cooperative positioning system. In the considered tests, where a high percentage of UWB communications was successful, the obtained results show that the car configuration can have a quite remarkable impact on the positioning performance, doubling the obtained median error.

Experimental Assessment of UWB and Vision-Based Car Cooperative Positioning System

The availability of global navigation satellite systems (GNSS) on consumer devices has caused a dramatic change in every-day life and human behaviour globally. Although GNSS generally performs well outdoors, unavailability, intentional and unintentional threats, and reliability issues still remain. This has motivated the deployment of other complementary sensors in such a way that enables reliable positioning, even in GNSS-challenged environments. Besides sensor integration on a single platform to remedy the lack of GNSS, data sharing between platforms, such as in collaborative positioning, offers further performance improvements for positioning. An essential element of this approach is the availability of internode measurements, which brings in the strength of a geometric network. There are many sensors that can support ranging between platforms, such as LiDAR, camera, radar, and many RF technologies, including UWB, LoRA, 5G, etc. In this paper, to demonstrate the potential of the collaborative positioning technique, we use ultra-wide band (UWB) transceivers and vision data to compensate for the unavailability of GNSS in a terrestrial vehicle urban scenario. In particular, a cooperative positioning approach exploiting both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) UWB measurements have been developed and tested in an experiment involving four cars. The results show that UWB ranging can be effectively used to determine distances between vehicles (at sub-meter level), and their relative positions, especially when vision data or a sufficient number of V2V ranges are available. The presence of NLOS observations is one of the principal factors causing a decrease in the UWB ranging performance, but modern machine learning tools have shown to be effective in partially eliminating NLOS observations. According to the obtained results, UWB V2I can achieve sub-meter level of accuracy in 2D positioning when GNSS is not available. Combining UWB V2I and GNSS as well V2V ranging may lead to similar results in cooperative positioning. Absolute cooperative positioning of a group of vehicles requires stable V2V ranging and that a certain number of vehicles in the group are provided with V2I ranging data. Results show that meter-level accuracy is achieved when at least two vehicles in the network have V2I data or reliable GNSS measurements, and usually when vehicles lack V2I data but receive V2V ranging to 2–3 vehicles. These working conditions typically ensure the robustness of the solution against undefined rotations. The integration of UWB with vision led to relative positioning results at sub-meter level of accuracy, an improvement of the absolute positioning cooperative results, and a reduction in the number of vehicles required to be provided with V2I or GNSS data to one.

FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of Vehicles

Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes network resources. To fill these gaps, this article proposes a vehicle cooperative positioning (CP) system based on federated learning (FedVCP), which makes full use of the potential of social Internet of Things (IoT) and collaborative edge computing (CEC) to provide high-precision positioning correction while ensuring user privacy. To the best of our knowledge, this article is the first attempt to solve the privacy of CP from a perspective of federated learning. In addition, we take the advantages of local cooperation through vehicle-to-vehicle (V2V) communications in data augmentation. For individual differences in vehicle positioning, we utilize transfer learning to eliminate the impact of such differences. Extensive experiments on real data demonstrate that our proposed model is superior to the baseline method in terms of effectiveness and convergence speed.

Mobile target localization and tracking techniques in harsh environment utilizing adaptive multi‐modal data fusion

Multi-source cooperative positioning systems relying on federated filtering have become attractive development directions of navigation strategy in real-time localization and tracking under challenging urban environment. However, local Kalman filters of traditional federated filtering may result in divergence when the system model or the measurements is inaccurate, and the fixed information distribution coefficient in federated filter cannot adaptively reflect the performance of each local filter. To improve the precision and robustness of the integrated navigation system, a novel adaptive federated strong tracking Kalman filter with dynamic fading factor mechanism for multi-sensor information fusion is proposed. Through iterative computation of the fading factor and updating the adaptive weight coefficients, strong tracking filter becomes robust to the uncertainty of system model. Meanwhile, an effective adaptive information distribution estimation algorithm based on the predicted residuals is constructed to balance the contributions of the kinematic model information and measurements on the state estimates. To ensure the stability of filtering, a simplified fusion strategy is established to solve the singular problem of the global covariance matrix of estimation error. Theoretical analysis and simulation results demonstrate the validity of the proposed approach in improving the accuracy and robustness of the integrated navigation and positioning systems. The proposed integrated multi-modal cooperative navigation and positioning algorithm will be of great significance to the implementation of real-time mobile target localization and tracking in harsh environment or dead zone.

An accurate cooperative positioning system for vehicular safety applications

Typical Global Navigation Satellite System (GNSS) receivers offer precision in the order of meters. This error margin is excessive for vehicular safety applications, such as forward collision warning, autonomous intersection management, or hard braking sensing. In this work we develop a Cooperative GNSS Positioning System (CooPS) that uses Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications to cooperatively determine absolute and relative position of the ego-vehicle with enough precision. To that end, we use differential GNSS through position vector differencing to acquire track and across-track axes projections, employing elliptical and spherical geometries. We evaluate CooPS performance by carrying out real experiments using off-the-shelf IEEE 802.11p equipment at the campus of the Federal University of Rio de Janeiro. We obtain an accuracy level under 1.0 and 1.5 m for track (where-in-lane) and across-track (which-lane) axes, respectively. These accuracy levels were achieved using a 2.5 m accuracy circular error probable (CEP) of 50% and a 5 Hz navigation update rate GNSS receiver.

Experimental Evaluation of a UWB-Based Cooperative Positioning System for Pedestrians in GNSS-Denied Environment.

Cooperative positioning (CP) utilises information sharing among multiple nodes to enable positioning in Global Navigation Satellite System (GNSS)-denied environments. This paper reports the performance of a CP system for pedestrians using Ultra-Wide Band (UWB) technology in GNSS-denied environments. This data set was collected as part of a benchmarking measurement campaign carried out at the Ohio State University in October 2017. Pedestrians were equipped with a variety of sensors, including two different UWB systems, on a specially designed helmet serving as a mobile multi-sensor platform for CP. Different users were walking in stop-and-go mode along trajectories with predefined checkpoints and under various challenging environments. In the developed CP network, both Peer-to-Infrastructure (P2I) and Peer-to-Peer (P2P) measurements are used for positioning of the pedestrians. It is realised that the proposed system can achieve decimetre-level accuracies (on average, around 20 cm) in the complete absence of GNSS signals, provided that the measurements from infrastructure nodes are available and the network geometry is good. In the absence of these good conditions, the results show that the average accuracy degrades to meter level. Further, it is experimentally demonstrated that inclusion of P2P cooperative range observations further enhances the positioning accuracy and, in extreme cases when only one infrastructure measurement is available, P2P CP may reduce positioning errors by up to . The complete test setup, the methodology for development, and data collection are discussed in this paper. In the next version of this system, additional observations such as the Wi-Fi, camera, and other signals of opportunity will be included.

Theoretical Limits on Cooperative Positioning in Mixed Traffic

A promising solution to meet the demands on accurate positioning and real-time situational awareness in future intelligent transportation systems (ITSs) is cooperative positioning, where vehicles share sensor information over the wireless channel. However, the sensing and communication technologies required for this will be gradually introduced into the market, and it is, therefore, important to understand what performance we can expect from cooperative positioning systems as we transition to a more modern vehicle fleet. In this paper, we study what effects a gradual market penetration has on cooperative positioning applications, through a Fisher information analysis. The simulation results indicate that solely introducing a small fraction of automated vehicles with high-end sensors significantly improves the positioning quality but is not enough to meet the stringent demands posed by safety critical ITS applications. Furthermore, we find that retrofitting vehicles with low-cost satellite navigation receivers and communication have marginal impact when the positioning requirements are stringent and that the longitudinal road position can be estimated more accurately than lateral.

A combined GPS UWB and MARG locationing algorithm for indoor and outdoor mixed scenario

An indoor or outdoor positioning system is hard to position in cross-region and complex environment. In order to make up for the loss of blind zone in scenario change, we proposed a cooperative positioning system based on GPS/UWB/MARG, which can achieve the seamless positioning between buildings in the hybrid scene. In the hybrid scene, using a weighted fusion algorithm to achieve the cooperative positioning of GPS and UWB, and MARG is used to improve the positioning accuracy of GPS. With data optimization and performance analysis of a subsystem, we process GPS/MARG data and UWB data by weighted fusion method. The optimal positioning information in different positioning environments will be output after independent judgment. The results show that, compared with GPS/MARG positioning system, the average positioning accuracy of the cooperative positioning system is improved by 64% in the hybrid scene. In addition, it expands the application scenario of a single positioning system.

An assessment on the use of stationary vehicles to support cooperative positioning systems

ABSTRACTIn this paper, we evaluate the ability of stationary vehicles (e.g. parked or temporary stopped cars) as tools to enhance the capabilities of existing cooperative positioning algorithms in vehicular networks. First, some real-world facts are provided to support the feasibility of our ideas. Then, we examine the idea in greater details in terms of the technical requirements and methodological analysis, and provide a comprehensive experimental evaluation using dedicated simulations. The routing of a drone through an urban scenario is presented as a non-traditional application case, where the benefits of the proposed approach are reflected in a better utilisation of the flight time.

A Fault-Tolerant Cooperative Positioning Approach for Multiple UAVs

Due to the increasing potentials and benefits of unmanned aerial vehicles (UAVs) in civil and surveillance applications, the cooperative flight using multiple UAVs has attracted more and more attention. However, fault-tolerant cooperative positioning for malfunctions in global positioning system (GPS) is one of the challenges that need to be addressed in various practical applications of UAVs. Motivated by solving this issue, this paper presents a reliable cooperative positioning approach for multiple UAVs cooperative flight by using the geometric azimuth angle and the inclination angle relative to the reference UAVs. In this proposed cooperative positioning system (COPS), these relative angles are measured through radio direction finding equipped on UAVs. With the horizontal dilution of precision of the various reference UAVs carefully analyzed, the optimal reference UAVs set can be first selected. Based on a constant acceleration kinematic model, an extended Kalman filter is then employed to improve the positioning accuracy. A COPS/GPS fault-tolerant navigation algorithm is finally developed to accommodate GPS fault. Simulation results are further presented to verify that the proposed algorithm can guarantee satisfactory navigation of the UAVs even when their GPS cannot work.

A DSRC Doppler/IMU/GNSS Tightly-coupled Cooperative Positioning Method for Relative Positioning in VANETs

Relative position awareness is a vital premise for the implementation of emerging intelligent transportation systems. However, commercial Global Satellite Navigation Systems (GNSS) receivers do not satisfy the requirements of these applications. Fortunately, Cooperative Positioning (CP) systems, based on inter-vehicle communications, have improved performance of relative positioning in a Vehicular Ad Hoc Network (VANET). CP techniques rely primarily on measurements from the Global Positioning System (GPS) to deliver measurements or positions that describe the location of individual vehicles. In urban environments, the reduced quality or complete unavailability of GPS measurements challenges the effectiveness of any CP algorithm. In this paper, a new enhanced tightly–coupled CP technique is presented by adding the measurements from low-cost inertial sensors and the Doppler shift of the carrier of Dedicated Short-Range Communications (DSRC) signals. In the enhanced CP method proposed here, vehicles communicate their Inertial Measurement Unit (IMU) data and GPS measurements. Each vehicle fuses the GPS measurements and IMU data and the inter-node range-rates based on the Doppler shift of the carrier of DSRC signals. Based on analytical and experimental results, in a full GPS coverage environment, the new tight integration CP outperforms tight CP with Inertial Navigation System (INS), tight CP and differential GPS by at least by 6%, 15%, and 28%, respectively. In a GPS outage, the performance improvement can be up to 60%, 55%, and 66% respectively.

Automatic large-scale three dimensional modeling using cooperative multiple robots

3D modeling of real objects by a 3D laser scanner has become popular in many applications, such as reverse engineering of petrochemical plants, civil engineering and construction, and digital preservation of cultural properties. Despite the development of lightweight and high-speed laser scanners, the complicated measurement procedure and long measurement time are still heavy burdens for widespread use of laser scanning. To solve these problems, a robotic 3D scanning system using multiple robots has been proposed. This system, named CPS-SLAM, consists of a parent robot with a 3D laser scanner and child robots with target markers. A large-scale 3D model is acquired by an on-board 3D laser scanner on the parent robot from several positions determined precisely by a localization technique, named the Cooperative Positioning System (CPS), that uses multiple robots. Therefore, this system can build a 3D model without complicated post-processing procedures such as ICP. In addition, this system is an open-loop SLAM system and a very precise 3D model can be obtained without closed loops. This paper proposes an automatic planning technique for a laser measurement by using CPS-SLAM. Planning a proper scanning strategy depending on a target structure makes it possible to perform laser scanning efficiently and accurately even for a large-scale and complex environment. The proposed technique plans an efficient scanning strategy automatically by taking account of several criteria, such as visibility between robots, error accumulation, and efficient traveling. We conducted computer simulations and outdoor experiments to verify the performance of the proposed technique.

Energy Efficient Cooperative Positioning Algorithm for Global Navigation Satellite System

Cooperative Positioning System (CPS) algorithm are suitable for GPS-challenged environments became a need of the hour because of its wide spread applications such as mobile communication. In cooperative positioning, Global Navigation Satellite System (GNSS) users or receivers exchange their GNSS data and information with their neighbors about their position. For CPS to be functioning GNSS user has to undergo three different task. The various estimation algorithms used in cooperative positioning are least squares, kalman filter, sum-product algorithm over a wireless network (SPWAN). These projects mainly focus on sum – product algorithm over a wireless network which can be implemented in a fully distributed way. Since it relies on a probabilistic, Bayesian approach algorithm are modeled as probability distribution functions. Experimental results shows reduced Localization error and minimum distance between the node. The power consumption of the nodes are reduced and thereby improving the battery life and increasing the throughput. Hence the lifetime of the network is been extended.