Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 22, Issue 6, Pages 3603-3615Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3018377
Keywords
Estimation; Colored noise; Covariance matrices; Eigenvalues and eigenfunctions; Roads; Software; Antenna arrays; Software defined internet of vehicles (SDN-IoV); non-cooperative UAV; autonomous vehicle; source enumeration; color noise
Categories
Funding
- Key Research and Development Program of Hainan Province [ZDYF2019011]
- National Natural Science Foundation of China [61701144, 61861015, 61801076, 61961013]
- Program of Hainan Association for Science and Technology Plans to Youth Research and Development Innovation [QCXM201706]
- Scientific Research Projects of University in Hainan Province [Hnky2018ZD-4]
- CAST [2018QNRC001]
- Tianjin University [HDTDU201906]
- Scientific Research Setup Fund of Hainan University [KYQD (ZR)1731]
- Hainan University [HDTDU201906]
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The paper proposes a novel system architecture using drones and SDN-IoV to address traffic issues, and introduces two source enumeration methods in complex environments. By utilizing signal subspace projection and eigen-subspace projection, the number of vehicles and pedestrians is estimated, showing excellent performance in color noise.
The traffic congestion and accidents can be relieved by deploying the software defined internet of vehicles (SDN-IoV). However, the traffic of pedestrians and vehicles is particularly heavy near commercial streets and campuses. In particular scenarios, the SDN-IoV may not ensure the quality of service (QoS) for pedestrians and vehicles. In this paper, we construct a novel system architecture consisting of multiple non-cooperative unmanned aerial vehicles (UAVs) and a SDN-IoV. The non-cooperative UAV is equipped with an antenna array to receive the signals from the vehicles and pedestrians of SDN-IoV. In order to locate the positions of vehicles and pedestrians, two source enumeration methods are proposed in a complex SDN-IoV environment with color noise. The projection matrix of the low dimensional signal subspace is constructed by the proposed criterion based on signal subspace projection (SSP). The sequence of the projected difference values of the local covariance matrix is applied to estimate the number of vehicles and pedestrians. The eigenvalues can be grouped to construct different subspaces by the proposed eigen-subspace projection (ESP). By projecting a new covariance matrix into the eigen-subspaces, the variance of values represents the projection difference can be exploited to estimate the number of vehicles and pedestrians. Simulation results and real system test verify the validity of the two proposed methods by comparing them with the state-of-the-art methods. Both of the methods have excellent estimation performance especially in color noise.
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