CAJ | 학술논문

　　分析比较了“当前”统计模型和Singer模型各自的特点，提出了基于改进的“当前”统计模型的模糊自适应车辆定位算法（MCS-FAEKF），实时动态选择机动模型和调整系统噪声协方差矩阵。相对于传统的“当前”统计模型卡尔曼滤波算法（CS-EKF）和Singer-EKF算法，MCS-FAEKF算法对车辆目标的定位精度和可靠性等都得到了较大提高，计算机仿真结果验证了算法的有效性和可行性。
　　분석비교료“당전”통계모형화Singer모형각자적특점，제출료기우개진적“당전”통계모형적모호자괄응차량정위산법（MCS-FAEKF），실시동태선택궤동모형화조정계통조성협방차구진。상대우전통적“당전”통계모형잡이만려파산법（CS-EKF）화Singer-EKF산법，MCS-FAEKF산법대차량목표적정위정도화가고성등도득도료교대제고，계산궤방진결과험증료산법적유효성화가행성。
The singer model and current statistical model were first analyzed and compared. A modified scheme based on the two kinds of models was proposed. Moreover, a modified current statistical model based-fuzzy adaptive extended Kalman filter (MCS-FAEKF) algorithm was proposed to choose maneuvering model and adjust system noise covariance dynamically. The simulated results show that the algorithm could get more accurate and reliable performance for vehicle positioning compared with the current statistical model based-extended Kalman filter (CS-EKF) and Singer-EKF algo-rithms.