Localization of vehicular ad-hoc networks with RSS based distance estimation

Received Signal Strength‐Based Localization for Vehicle Distance Estimation in Vehicular Ad Hoc Networks (VANETs)

“…The magnitude detection characteristics and scalar creative matrix construction of this approach enable a closed-form answer that has been discovered to be resilient to severe measurement noise. The authors of [14,19] detail a unique automobile network for VANETs. Nowadays that these vehicles are interconnected with one another and the road infrastructure, they can be used for a wide range of purposes, like driving Internet access and enhancing transportation security.…”

“…(i) This paper's contribution is the novel closed-form localization approach, and it suggests for VANETs, which enables us to estimate the positions of several vehicles (ii) The anchor RSS measurements at the RSUs are the key source of data for the proposed technique. It takes into account a completely connected VANET, which is a typical assumption for network localization [13] (iii) The CRLB, which is the lower bound on the error variance, is also calculated for the suggested closed-form solution [14] (iv) The theoretical mean square error for the suggested closed-form solution is also calculated.…”

Infrastructure Wi-Fi for connected autonomous vehicle positioning: A review of the state-of-the-art

“…RSS measurements between target vehicles and four RSUs were also evaluated via simulation in [48]. In the simulation setup, the RSUs were placed at the corners of 4 corners of a square region (30 by 30).…”

Roadside Units-Enabled Cooperative Localization Scheme for Autonomous Vehicles Under Gnss-Denied Environments: Methodology and Demonstration

“…Table I summarizes existing sate-of-the-art methods developed to improve V2X-based positioning accuracy in response to challenges (i) through (iii). To address challenge (i), Zhu et al [31], Saeed et al [24], and Ma et al [32] used Taylor series expansions, linear expansions, and weighted position information to approximate the original nonlinear equation system as a set of linear equations relating to the vehicle's position. However, this least squares (LS)-based framework is ineffective when measurement noise variance is significant.…”