Cooperative Positioning Methods Research Articles

A whole-net cooperative positioning method based on CDGNSS and inter-vehicle ranging

Relative positioning is recognized as an important issue for vehicles in urban environments. Multi-vehicle Cooperative Positioning (CP) techniques which fuse the Global Navigation Satellite System (GNSS) and inter-vehicle ranging have attracted attention in improving the performance of baseline estimation between vehicles. However, current CP methods estimate the baselines separately and ignore the interactions among the positioning information of different baselines. These interactions are called ‘information coupling’. In this work, we propose a new multi-vehicle precise CP framework using the coupled information in the network based on the Carrier Differential GNSS (CDGNSS) and inter-vehicle ranging. We demonstrate the benefit of the coupled information by deriving the Cramer-Rao Lower Bound (CRLB) of the float estimation in CP. To fully use this coupled information, we propose a Whole-Net CP (WN-CP) method which consists of the Whole-Net Extended Kalman Filter (WN-EKF) as the float estimation filter, and the Partial Baseline Fixing (PBF) as the ambiguity resolution part. The WN-EKF fuses the measurements of all baselines simultaneously to improve the performance of float estimation, and the PBF strategy fixes the ambiguities of the one baseline to be estimated, instead of full ambiguity resolution, to reduce the computation load of ambiguity resolution. Field tests involving four vehicles were conducted in urban environments. The results show that the proposed WN-CP method can achieve better performance and meanwhile maintain a low computation load compared to the existing methods.

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.

Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV's Moving Vector.

The traditional leader-follower Unmanned Aerial Vehicle (UAV) formation cooperative positioning (CP) algorithm, based on relative ranging, requires at least four leader UAV positions to be known accurately, using relative distance with leader UAVs to achieve the unknown position follower UAV’s high-precision positioning. When the number of the known position leader UAVs is limited, the traditional CP algorithm is not applicable. Aiming at the minimum cooperative unit, which consists of a known position leader UAV and an unknown position follower UAV, this paper proposes a CP method based on the follower UAV’s moving vector. Considering the follower UAV can only acquire the single distance with the leader UAV at each distance-sampling period, it is difficult to determine the follower UAV’s spatial location. The follower UAV’s moving vector is used to construct position observation of the follower UAV’s inertial navigation system (INS). High-precision positioning is achieved by combining the follower UAV’s moving vector. In the process of CP, the leader UAV obtains a high-precision position by an INS/Global Positioning System (GPS) loosely integrated navigation system and transmits its position information to the follower UAV. Based on accurate modeling of the follower UAV’s INS, the position, velocity and heading observation equation of the follower UAV’s INS are constructed. The improved extended Kalman filtering is designed to estimate the state vector to improve the follower UAV’s positioning accuracy. In addition, considering that the datalink system based on radio signals may be interfered with by the external environment, it is difficult for the follower UAV to obtain relative distance information from the leader UAV in real time. In this paper, the availability of the relative distance information is judged by a two-state Markov chain. Finally, a real flight test is conducted to validate the performance of the proposed algorithm.

A Cooperative Positioning Method of Connected and Automated Vehicles with Direction-of-Arrival and Relative Distance Fusion

The rapid development of science and technology has created favorable conditions for Connected and Automated Vehicles (CAVs). Accurate localization is one of the fundamental functions of CAV to realize some advanced operations such as vehicle platooning. However, complicated urban traffic environments, such as the flyover, significantly influence vehicular positioning accuracy. The inability of CAV to accurately perceive self-localization information has become an urgent issue to be addressed. This paper proposed a novel cooperative localization method by introducing the relative Direction-of-Arrival (DOA) and Relative Distance (RD) into CAV to improve the localization accuracy of CAV in the multivehicle environment. First, the three-dimensional positioning error model of the host vehicle concerning adjacent vehicles in azimuth angle and pitch angle and intervehicle distances under the vehicle-to-vehicle communication was established. Second, two least-squares estimation algorithms, linear and nonlinear, are established to decrease the position errors by combining relative DOA and RD measurement information. To verify the proposed algorithm's effect, the PreScan-Simulink joint simulation is carried out. The results show that the host vehicle's localization accuracy by the proposed method can be improved by 25% compared with direct linearization. Besides, by combining relative DOA and relative RD measurement, the locating capability of the least-square-based nonlinear optimization method can be enhanced by 22%.

Research on Roadside Unit-Assisted Cooperative Positioning Method for a Connected Vehicle Environment

With the rapid development of intelligent transportation systems and connected vehicle (CV) technology, vehicle-to-infrastructure communication technologies have provided new solutions to traditional traffic safety and efficiency issues. However, the current intelligent CVs often provide positioning services only through a single GPS. These modules’ positioning accuracy can be insufficient to support the safety and reliability of security applications. The question arises of how to enhance GPS positioning accuracy in a CV environment without adding additional equipment and using only the information that existing CV devices can access. This paper proposes a roadside unit (RSU)-assisted GPS-RSS (received signal strength) cooperative positioning method for a CV environment. The rough position information from GPS is combined with RSS ranging and dead reckoning to obtain preliminary position estimated coordinates of the CV. Bayesian filtering is performed to obtain a more accurate preliminary position estimate. The final position estimated coordinates, obtained after data fusion, are combined with the high-precision map data (MAP) sent by the RSU to match the lane where the vehicle is located. Simulation and field tests verify each other, and the results show that the lane positioning accuracy of GPS can be improved by 21% within the range from the RSU to the CV’s on-board unit.

Cooperative Positioning for V2X Applications Using GNSS Carrier Phase and UWB Ranging

With the evolution of communication techniques, vehicle-to-vehicle (V2V) communications with low latency and high speed facilitate the study of cooperative positioning (CP) methods for vehicular applications. However, the current CP methods are incapable of bridging the gap between positioning accuracy required for crucial vehicular applications and what CP provides. The CP method with carrier-phase-based Real Time Kinematic (RTK) principle becomes a potential candidate for high-precision positioning. In this letter, we reveal a cooperative navigation filtering (CNF) algorithm fusing Global Navigation Satellite System (GNSS) data and Ultra-wideband (UWB) ranging data to achieve decimeter-level positioning. The main novelty of the proposed CP method is to include centimeter-level GNSS carrier phase information and derive a collaborative ambiguity resolution model. Numerical results and comparisons validate the feasibility and superiority of proposed method.

Cooperative positioning for low-cost close formation flight based on relative estimation and belief propagation

Cooperative Positioning (CP) is considered to be a promising method for close formation flight, which can fully exploit the navigation information in a network. This work proposes a new CP framework based on relative position estimation and optimized belief propagation (BP), aiming to enhance the absolute positioning accuracy for low-cost UAVs. Relative estimation is used to augment the BP estimation, which can fully exploit the onboard sensors. For BP, it is widely used as a CP estimator. This work proposes an optimized BP based on vectorized message passing and message evaluation, which can make an effective balance between computational load and estimation accuracy. Simulation results show that the proposed CP framework has better estimation accuracy than traditional CP methods, and has lower computational load than the best existing non-parametric BP (NBP) method with equivalent accuracy.

An Empirical Study on V2X Enhanced Low-Cost GNSS Cooperative Positioning in Urban Environments.

High-precision and lane selective position estimation is of fundamental importance for prospective advanced driver assistance systems (ADAS) and automated driving functions, as well as for traffic information and management processes in intelligent transportation systems (ITS). User and vehicle positioning is usually based on Global Navigation Satellite System (GNSS), which, as stand-alone positioning, does not meet the necessary requirements in terms of accuracy. Furthermore, the rise of connected driving offers various possibilities to enhance GNSS positioning by applying cooperative positioning (CP) methods. Utilizing only low-cost sensors, especially in urban environments, GNSS CP faces several demanding challenges. Therefore, this contribution presents an empirical study on how Vehicle-to-Everything (V2X) technologies can aid GNSS position estimation in urban environments, with the focus being solely on positioning performance instead of multi-sensor data fusion. The performance of CP utilizing common positioning approaches as well as CP integration in state-of-the-art Vehicular Ad-hoc Networks (VANET) is displayed and discussed. Additionally, a measurement campaign, providing a representational foundation for validating multiple CP methods using only consumer level and low-cost GNSS receivers, as well as commercially available IEEE 802.11p V2X communication modules in a typical urban environment is presented. Evaluating the algorithm’s performance, it is shown that CP approaches are less accurate compared to single positioning in the given environment. In order to investigate error influences, a skyview modelling seeking to identify non-line-of-sight (NLoS) effects using a 3D building model was performed. We found the position estimates to be less accurate in areas which are affected by NLoS effects such as multipath reception. Due to covariance propagation, the accuracy of CP approaches is decreased, calling for strategies for multipath detection and mitigation. In summary, this contribution will provide insights on integration, implementation strategies and accuracy performances, as well as drawbacks for local area, low-cost GNSS CP in urban environments.

On the Accuracy of Inter-Vehicular Range Measurements Using GNSS Observables in a Cooperative Framework

Many vehicular applications such as navigation, collision avoidance, and location-based services envisioned under intelligent transportation system (ITS) framework require position information with certain accuracy and reliability. Global navigation satellite system (GNSS) are widely deployed for absolute and relative positioning in a wide range of ITS applications due to their global coverage but these systems have limited accuracy and availability to meet the requirement of many safety-critical applications. This limitation has led to the development of cooperative positioning (CP) methods in a vehicular ad-hoc network. CP methods are based on the assumption that the inter-vehicular ranges are available through some radio ranging method, such as received signal strength, time of arrival, or time difference of arrival. However, the feasibility and accuracy of these radio ranging methods is limited due to the constraints of communication medium and rapidly changing vehicular environment. In this paper, we propose a generalized version of a theoretical framework for the measurement and analysis of inter-vehicular ranges by exchanging GNSS observables between vehicles. More specifically, we investigate the utilization of GNSS observables in the following four categories for range measurement: 1) absolute positions of the vehicles provided by GNSS receivers; 2) raw code pseudoranges; 3) single difference of the raw code pseudoranges; and 4) double difference of the raw code pseudoranges. The developed theoretical framework compares the accuracy of inter-vehicular range measurement using these four observables. This framework is further validated by actual field trials in different mobile environments using two vehicles equipped with GNSS receivers.

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.

A Low-cost IMU/GNSS Cooperative Positioning Method for VANETs in the Urban Environments

It is important to know the key problem of the emerging intelligent transportation systems –relative position, especially in the collision warning and speeds advisory. However, the commercial Global Satellite Navigation Systems (GNSS) receiver cannot up to the standard of these purpose. Fortunately, using Cooperative Positioning (CP) techniques to share the GNSS measurements between vehicles can largely improve the performance of relative positioning in a vehicular ad hoc network (VANET). But in urban environments, the reduced quality or complete unavailability of GNSS measurements challenge the effectiveness of any CP algorithm. In this paper, a new improved tight CP technique is proposed which adds the measurements from low cost inertial sensors. In the enhanced CP method proposed here, vehicles communicate their GPS measurements and inertial measurement unit (IMU) data, and each vehicle fuses local GPS and IMU measurements and those of the neighbours. Experimental results show that the new tight integration CP method can enhance the relative positioning in low GPS coverage environment, such as in very dense urban areas and tunnels.

An ultra-wide bandwidth-based range/GPS tight integration approach for relative positioning in vehicular ad hoc networks

Relative position awareness is a vital premise for the implementation of emerging intelligent transportation systems, such as collision warning. However, commercial global navigation satellite systems (GNSS) receivers do not satisfy the requirements of these applications. Fortunately, cooperative positioning (CP) techniques, through sharing the GNSS measurements between vehicles, can improve the performance of relative positioning in a vehicular ad hoc network (VANET). In this paper, while assuming there are no obstacles between vehicles, a new enhanced tightly coupled CP technique is presented by adding ultra-wide bandwidth (UWB)-based inter-vehicular range measurements. In the proposed CP method, each vehicle fuses the GPS measurements and the inter-vehicular range measurements. Based on analytical and experimental results, in the full GPS coverage environment, the new tight integration CP method outperforms the INS-aided tight CP method, tight CP method, and DGPS by 11%, 15%, and 24%, respectively; in the GPS outage scenario, the performance improvement achieves 60%, 65%, and 73%, respectively.

An Enhanced UWB-Based Range/GPS Cooperative Positioning Approach Using Adaptive Variational Bayesian Cubature Kalman Filtering

Precise position awareness is a fundamental requirement for advanced applications of emerging intelligent transportation systems, such as collision warning and speed advisory system. However, the achievable level of positioning accuracy using global navigation satellite systems does not meet the requirements of these applications. Fortunately, cooperative positioning (CP) techniques can improve the performance of positioning in a vehicular ad hoc network (VANET) through sharing the positions between vehicles. In this paper, a novel enhanced CP technique is presented by combining additional range-ultra-wide bandwidth- (UWB-) based measurements. Furthermore, an adaptive variational Bayesian cubature Kalman filtering (AVBCKF) algorithm is proposed and used in the enhanced CP method, which can add robustness to the time-variant measurement noise. Based on analytical and experimental results, the proposed AVBCKF-based CP method outperforms the cubature Kalman filtering- (CKF-) based CP method and extended Kalman filtering- (EKF-) based CP method.

Relative Positioning Enhancement in VANETs: A Tight Integration Approach

Position information is a fundamental requirement for many vehicular applications such as navigation, intelligent transportation systems (ITSs), collision avoidance, and location-based services (LBSs). Relative positioning is effective for many applications, including collision avoidance and LBSs. Although Global Navigation Satellite Systems (GNSSs) can be used for absolute or relative positioning, the level of accuracy does not meet the requirements of many applications. Cooperative positioning (CP) techniques, fusing data from different sources, can be used to improve the performance of absolute or relative positioning in a vehicular ad hoc network (VANET). VANET CP systems are mostly based on radio ranging, which is not viable, despite being assumed in much of the literature. Considering this and emerging vehicular communication technologies, a CP method is presented to improve the relative positioning between two vehicles within a VANET, fusing the available low-level Global Positioning System (GPS) data. The proposed method depends on no radio ranging technique. The performance of the proposed method is verified by analytical and experimental results. Although the principles of the proposed method are similar to those of differential solutions such as differential GPS (DGPS), the proposed technique outperforms DGPS with about 37% and 45% enhancement in accuracy and precision of relative positioning, respectively.

A DSRC Doppler-Based Cooperative Positioning Enhancement for Vehicular Networks With GPS Availability

Position information, as a fundamental element for many of the modern vehicle-based logistic applications, is comprehensively provided by Global Navigation Satellite Systems (GNSS) such as the Global Positioning System (GPS). A variety of applications, including navigation and intelligent transpiration systems, require position data with certain accuracy. However, the shortcomings of GNSS, such as limited accuracy and availability, have been a motivation for recently emerging Cooperative Positioning (CP) methods based on vehicle–vehicle and vehicle–infrastructure communications. The majority of earlier CP methods assume the availability of distances between the participating nodes as a main parameter, using common techniques of radio ranging such as the Received Signal Strength (RSS), Time of Arrival (TOA), and Time Difference of Arrival (TDOA). However, the feasibility of these radio-ranging methods in the harsh environment of vehicular networks is questionable. Avoiding the radio-ranging challenges, in this paper, a new CP method is presented to improve the GPS estimates using internode range-rates based on the Doppler shift of the carrier of Dedicated Short-Range Communications (DSRC) signals, which is the nominated medium for vehicular communication. Depending on the speed of the participating vehicles and traffic intensity, improvement of up to 48% over the GPS accuracy is achieved. Because the Doppler effect is used, relative mobility of the nodes, which is generally a challenge for radio-ranging techniques, is a requirement for the proposed method, making it a more suitable solution for vehicular applications. Addressing the viability of the proposed technique, Doppler-based range-rating is verified in practice using DSRC transceivers. One section of highway is surveyed and modeled for simulation. In addition, GPS estimates are provided by feeding GPS signals, generated by a GPS signal generator, to a real GPS receiver.