# A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
## 1 Introduction  
As a part of the vehicular network, vehicle positioning plays an important role in intelligent traffic management systems, vehicle detection, autonomous driving, intelligent parking, and so on [1]. Traditional vehicle positioning usually adopts global navigation satellite system (GNSS) represented by global positioning system (GPS), GLONASS, Beidou navigation system (BDS), and Galileo [2; 3; 4; 5; 6; 7]. However, in urban canyon environments, GNSS signals are often blocked by high-rise buildings and perform with low capability in terms of positioning [8]. Mobile communication networks can fill the gaps in positioning performance of GNSS in urban environment due to its widely distribution and high signal transmission quality [9; 10; 11; 12]. However, it lacks hear-ability (the capability of detecting the number of base stations). One of the striking features of fifth generation mobile networks (5G) is ultra-dense network (UDN) which improves the hear-ability of mobile communication network. Therefore, UDN brings the opportunity of 5G vehicle positioning in urban canyon environments. The 5G vehicle positioning method can be divided into the following categories: the method based on observed time difference of arrival (OTDOA) [13], angle of arrival (AOA)-based method [14; 15], joint AOA ranging method [16; 17], and so on. Both OTDOA and joint AOA-ranging methods require accurate time delay estimation of 5G signals to improve positioning performance. The time delay estimation algorithm can be divided into two categories: correlation peak detection-based algorithm [12] and super-resolution-based algorithm [18; 19; 20; 21; 22]. Super-resolution-based algorithm has higher time delay estimation accuracy than correlation peak detection-based algorithm. As a popular algorithm of super-resolution-based algorithm, multiple signal classification (MUSIC) algorithm estimates time delay by the orthogonality between the steering vector of received baseband signal and noise subspace of received baseband signal's covariance matrix. MUSIC based time delay estimation methods have been studied in recent years, [PERSON] et al. [23] applied super-resolution spectral estimation techniques to the measured channel frequency response to accurately estimate TOA for indoor geolocation applications. The simulation results show that super-resolution techniques can significantly improve the performance of TOA estimation as compared with conventional techniques including direct IFT and DSSS signal-based cross-correlation techniques. [PERSON] et al. [24] proposed a frequency-domain-based cross-spectral super-resolution time delay estimate algorithm, and the method performed an accurate time delay estimation in the multipath channel model that they proposed in the paper. [PERSON] et al. [25] compared the performance of the super-resolution method with MUSIC and a traditional cross-correlation algorithm in a simulated indoor multipath TOA environment, the simulation results showed that TOA estimation with time domain channel estimation can approach high level accuracy and stability. [PERSON] et al. [26] converted time delay estimation into a sinusoidal parameter estimation problem, and the sinusoidal parameters are estimated by generalizing the MUSIC algorithm. Simulation results showed that the proposed algorithm performs better than the classical correlation approach and the conventional MUSIC method for the closely spaced components in multipath environments. [PERSON] et al. [27] compared the estimation of signal parameters via the rotational invariance technique (ESPRIT), root-multiple signal classification (RootMUSIC), and matrix pencil (MP) for ranging. The simulation results show that RootMUSIC and MP algorithm demonstrate better performance in ranging. [PERSON] et al. [28] used a two-dimensional MUSIC algorithm to estimate TOA and AOA in a IR-UWB system, and the simulation results showed that the performance is better than RootMUSIC and matrix pencil algorithm. [PERSON] et al.