Abstract
Reliable and precise vehicle positioning is essential for most intelligent transportation applications as well as autonomous driving. Due to satellite signal blocking, it can be challenging to achieve continuous lane-level positioning in GPS-denied environments such as urban canyons and crossroads. In this paper, a positioning strategy utilizing ultra-wide band (UWB) and low-cost onboard sensors is proposed, aimed at tracking vehicles in typical urban scenarios (such as intersections). UWB tech offers the potential of achieving high ranging accuracy through its ability to resolve multipath and penetrate obstacles. However, not line of sight (NLOS) propagation still has a high occurrence in intricate urban intersections and may significantly deteriorate positioning accuracy. Hence, we present an autoregressive integrated moving average (ARIMA) model to first address the NLOS problem. Then, we propose a tightly-coupled multi sensor fusion algorithm, in which the fuzzy calibration logic (FCL) is designed and introduced to adaptively adjust the dependence on each received UWB measurement to effectively mitigate NLOS and multipath interferences. At last, the proposed strategy is evaluated through experiments. Ground test results validate that this low-cost approach has the potential to achieve accurate, reliable and continuous localization, regardless of the GPS working statue.
Highlights
With the rapid development of transportation worldwide, it has become essential to realize accurate and reliable vehicle self-localization for many guidance and safety-related applications, such as intelligent transportation systems (ITS), advanced driver assistance systems (ADAS) and autonomous driving [1,2].Traditional vehicle positioning, which utilizes low-cost onboard sensors, for instance, microelectromechanical Inertial Navigation System (MEMS-INS), electronic compass and odometer, can provide continuity and availability in some cases
Earth. ∆t is the sampling time. g is the acceleration of gravity. γw is the reciprocal of the autocorrelation time for the scale factor of the wheel speed. βz is the reciprocal of the autocorrelation time for the gyroscope’s stochastic drift. σw is the variance of the noise associated with the wheel speed. σz is the variance of the noise associated with the gyroscope’s stochastic drift
Step (2) Estimate the priori state and its error covariance and calculate the predict observation based on Equations (22)~(26); based on Equations (22)~(26); Step (3) Obtain the whole adaptive noise covariance matrix by Equations (32) and (33); Step (4) Calculate the unscented Kalman filter (UKF) gain according to Equations (27)~(29); Step (5)
Summary
With the rapid development of transportation worldwide, it has become essential to realize accurate and reliable vehicle self-localization for many guidance and safety-related applications, such as intelligent transportation systems (ITS), advanced driver assistance systems (ADAS) and autonomous driving [1,2]. Aiming at tracking vehicles in typical urban scenarios as intersections, this paper proposes a tightly coupled integration strategy using UWB, GPS and MEMS-INS. In this strategy, the algorithms for both UWB raw measurements’ preprocessing and global fusion are developed to obtain a better performance. TheProposed main advantages of utilizing to UWB realize vehicle positioning in road junctions are asDue follows: to its ability to resolve multipath and penetrate obstacles, UWB technology offers the potential of achieving high ranging accuracy through time of arrive (TOA) measurements even in (a) Its extremely large bandwidth, usually between 3.6~10.1 GHz, makes it robust and more resistant trash environments.
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