Improving Cooperative Positioning for Vehicular Networks

Assisting drivers during overtaking using Car-2-Car communication and multi-agent systems

“…The standard deviation of GPS-based positioning error is σ P = 7m and standard deviation of ranging measurements error between two vehicles σ R = 5m in [19] . The ranging measurements are periodically exchange location, speed, and other kinematic information.…”

“…Using network improvements methods to reduce communication overhead in VANETS -due to periodically exchange of messages (for example ranging measurements), can reduce packet loss and improve positioning accuracy. In [19] several methods have been studied: piggyback, compression, reducing broadcasting interval and network coding. The combination of network improvements methods with the CP technique brought a 40% improvement in positioning accuracy.…”

Localization in vehicular ad hoc networks using data fusion and V2V communication

“…The distance can 103 be further estimated by measuring the Time Difference Of Arrival 104 (TDOA) between a pair of receivers. 105 In [10], Yao et al propose a cooperative positioning (CP) method 106 which fuses kinematics information obtained from GPS or other 107 kinematic sensors, with the distance measurements calculated 108 based on radio-ranging techniques such as TOA and TDOA. The 109 vehicles positions accuracy within each vehicle cluster have been 110 further improved by using a routing algorithm in [11].…”

“…VANET 5 can be viewed as an intelligent component of Intelligent Transporta- 6 tion Systems (ITS) as the vehicles communicate with each other as 7 well as with the roadside base stations/roadside units (RSUs) located 8 at critical points of the road, such as intersections or construction 9 sites. VANETs differ notably from other types of ad-hoc networks, 10 such as wireless sensor networks (WSNs) or mobile ad-hoc networks 11 (MANETs) in terms of node dynamics and heterogeneity. The compre- 12 hensive well-organized VANETs are responsible for extracting, man- 13 aging and interpreting the information to achieve knowledge, and 14 making it available for travelers.…”

Robust all-source positioning of UAVs based on belief propagation

“…A big step forward for Kalman-based filtering is the invention of unscented Kalman filtering [10][11][12]. In cooperative positioning [13][14][15][16][17][18][19][20], nodes have not only pseudoranges from navigation satellites but also ranging information with wireless peers. Existing algorithms such as iterative least square and Kalman filters can be extended to cooperative positioning, which leads to cooperative least square and cooperative Kalman filtering algorithm [21].…”