## 2 Literature Review  
Scholars have conducted much research on vehicle positioning technology. Based on GPS data and fuzzy rules, [PERSON] used map matching to realize vehicle positioning [1]. [PERSON] researched the method of using radio frequency identification (RFID) technology to locate vehicles when triangulation cannot work [2]. [PERSON] applied the combination of GPS and the global system for mobile communications (GSM) to improve the accuracy of vehicle positioning [3]. [PERSON] estimated the congestion level of rail transit based on smart card data and vehicle location data [4]. [PERSON] exploited the Vehicular Ad Hoc Network (VANET) paired with unmanned aerial vehicles (UAVs) to carry out real-time positioning research on vehicles without fixed base stations [5]. [PERSON] designed a wireless sensor network to locate terminals. Once the target was close to a sensor in the network, the target could be located [6]. [PERSON] utilized fixed-wing UAVs to visually recognize the vehicle and locate the vehicle accordingly [7]. [PERSON] employed mileage data and a visual assistance system to locate the vehicle without using external sensor signals [8]. [PERSON] proposed a location method based on the noise location of base station data [9]. [PERSON] researched related positioning algorithms based on the signal strength data of the GSM/3G network [10]. [PERSON] proposed a method of using the strength of the mobile signal to locate the mobile terminal [11]. [PERSON] evaluated the accuracy of mobile location data based on mobile signals [12]. [PERSON] suggested a mobile positioning method based on the attenuation difference of base station signals and verified the method [13]. [PERSON] presented that mobile positioning data has important application value for intelligent transportation systems [14]. [PERSON] implied a terminal positioning method based on dynamic mobile positioning [15]. [PERSON] proposed a positioning method based on code division multiple access data [16]. In the latest research, [PERSON] predicted the vehicle location through the prediction method; his research locates the vehicle by combining short-term accurate vehicle trajectory prediction and long-term rough vehicle trajectory prediction [17]. In a situation of conventional positioning methods fail in vehicle positioning, [PERSON] proposed a method that uses inertial navigation as a positioning supplement. The vehicle location can be estimated by combining the vehicle's motion state with GPS positioning [18]. [PERSON] transformed the vehicle positioning problem to a Direction-Of-Arrival (DOA) estimation and proposed a non-convex algorithm to solve the optimization problem [19]. There are also many related types of research concerning the improvement of positioning accuracy. [PERSON] designed and verified a set of mobile location adaptive update solutions [20]. [PERSON] illustrated an NLOS positioning method to improve positioning accuracy [21]. [PERSON] combined the positioning methods of time difference of arrival (TDOA) and angle of arrival (AOA) and proposed a more accurate positioning method [22]. [PERSON] adapted the Kalman filter to process positioning data and obtained a better processing result [23]. [PERSON] distinguished and optimized the near-field positioning algorithm and the far-field positioning algorithm [24]. [PERSON] reduced the errors in positioning and improved positioning accuracy through the weighted least-squares method [25]. [PERSON] explained the method of choosing a suitable sensor to measure the moving position [26]. [PERSON] improved the accuracy of location data acquisition through a weighting matrix [27]. [PERSON] suggested a two-stage method that combines calibrating the sensor and acquiring position data, which improved the accuracy of the positioning [28]. [PERSON] conducted further research on the use of the least-squares method to eliminate position error [29]. [PERSON] researched methods to eliminate the delay of positioning information and improve the accuracy of the positioning [30].