CAJ | 학술논문

为提高车辆间自组织网（VANET）中车辆定位的精度，提出了一种基于车辆间协作和多普勒频移的算法（CDCFO），并采用无迹卡尔曼滤波器（UKF）来进行车辆定位。多车辆间相互关联的状态和测量信息，如位置、速度以及专用近程车间通信（DSRC）信号的多普勒频移等，通过协作共享可以被有效融合和处理，来提高对车辆运动状态估计和预测的准确度；UKF 直接使用非线性测量方程，因此可以避免扩展卡尔曼滤波器（EKF）在对非线性测量方程进行线性化时忽略高阶项带来的误差。计算机分析和仿真结果表明，相对于常规 GPS 和 CDCFO-EKF 算法，提出的CDCFO-UKF 算法对车辆目标的位置估计平均误差和均方根误差等定位性能都有较大提高。
위제고차량간자조직망（VANET）중차량정위적정도，제출료일충기우차량간협작화다보륵빈이적산법（CDCFO），병채용무적잡이만려파기（UKF）래진행차량정위。다차량간상호관련적상태화측량신식，여위치、속도이급전용근정차간통신（DSRC）신호적다보륵빈이등，통과협작공향가이피유효융합화처리，래제고대차량운동상태고계화예측적준학도；UKF 직접사용비선성측량방정，인차가이피면확전잡이만려파기（EKF）재대비선성측량방정진행선성화시홀략고계항대래적오차。계산궤분석화방진결과표명，상대우상규 GPS 화 CDCFO-EKF 산법，제출적CDCFO-UKF 산법대차량목표적위치고계평균오차화균방근오차등정위성능도유교대제고。
To improve the vehicle positioning accuracy in the vehicular ad hoc network (VANET), a new positioning algorithm based on inter-vehicle cooperation and Doppler carrier frequency offset (CDCFO)is put forward.Meanwhile,an unscented Kalman filter (UKF)is applied to the algo-rithm.The relative state and measure information of multi-vehicles,such as location,velocity, Doppler carrier frequency offset of the dedicated short range communication (DSRC)signal and so on,are effectively fused and processed by cooperation and sharing,in order to improve the estima-ting and predicting accuracy of the vehicle state.Since the UKF directly uses the nonlinear measure-ment equation,it can avoid the error which is caused by omitting the high-level items during the lin-earization of the nonlinear measurement equation in the extended Kalman filter (EKF).The analytic and simulated results show that,compared with the common global positioning system(GPS)and the CDCFO-EKF algorithm,the performance of the proposed CDCFO-UKF algorithm such as the mean error and the root mean square error of the vehicle is improved greatly.