Abstract
With the improvement of living standard and the development of science and technology, Internet of Vehicle (IOV) will play an important part in industrial transportation as a main research field of Internet of Things. As a result, it is very necessary to grasp the location of vehicle. However, the traditional single global position system is easily affected by the external environment, so an accessorial locating approach based on wideband direction of arrival (DOA) estimation in intelligent transportation is proposed. First, model the array received signal on the road infrastructure. Then, by means of random forest regression (RFR) in the supervised learning, upper triangle elements of the covariance matrix of each frequency and the actual DOA are, respectively, extracted as the input features and output parameters; thus, the corresponding prediction coefficients are solved by training. After that, the trained RFR model can be used to calculate the final direction using test samples. Finally, these vehicles can be located according to the geometrical relation between the vehicle and the infrastructure. The proposed algorithm is not only suitable for uncorrelated signals but also for uncorrelated and correlated mixed signals without wideband focusing. The simulations show that compared with some sparse recovery algorithm, the prediction accuracy and resolution are effectively improved.
Highlights
Nowadays, automobile traffic has become an indispensable part of our modern industry, and vehicle-related technologies and industries are becoming increasingly mature [1,2,3,4]
(2) In this paper, signal information of different frequencies is extracted as input feature, while direction of arrival (DOA) is taken as the output for training; DOA of vehicle is estimated by random forest regression (RFR); the prediction model can be adjusted through parameter optimization
The general idea of vehicle locating based on DOA estimation is summed up as follows: First, the feature data sample related to DOA estimation is acquired
Summary
Automobile traffic has become an indispensable part of our modern industry, and vehicle-related technologies and industries are becoming increasingly mature [1,2,3,4]. We can estimate their DOA and locate these vehicles according to geometrical relationship of the signals and the infrastructure It improves the accuracy and enhances the resolution for the target, which is very suitable for multiple vehicles (2) In this paper, signal information of different frequencies is extracted as input feature, while DOA is taken as the output for training; DOA of vehicle is estimated by RFR; the prediction model can be adjusted through parameter optimization. The RFR is especially suitable for small snapshots (3) In practical applications, multiple vehicles will be possibly close to one another on the road, and multipath transmission often exists, leading to a large number of correlated or even coherent signals in the locating process In such an environment, the performance of traditional DOA estimation algorithms will decline sharply, or even fail. The algorithm proposed in this paper is suitable for uncorrelated signals, as well as the two coexisting scenes of uncorrelated and coherent signals without wideband focusing
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
