CAJ | 학술논문

阐述了卡尔曼滤波（KF）和扩展卡尔曼滤波（EKF）的原理和方法，建立了无源定位系统的状态模型和观测模型，推导了将非线性观测模型线性化，并利用EKF进行递推滤波估计的步骤和公式。通过计算机仿真，验证了运用EKF算法解决基于方位角及其变化率测量信息的无源定位方法，结果表明，运用EKF滤波算法，可以实现单观测站对运动目标的无源定位，初始状态估计误差对定位收敛的性能有较大影响。
천술료잡이만려파（KF）화확전잡이만려파（EKF）적원리화방법，건립료무원정위계통적상태모형화관측모형，추도료장비선성관측모형선성화，병이용EKF진행체추려파고계적보취화공식。통과계산궤방진，험증료운용EKF산법해결기우방위각급기변화솔측량신식적무원정위방법，결과표명，운용EKF려파산법，가이실현단관측참대운동목표적무원정위，초시상태고계오차대정위수렴적성능유교대영향。
The principle and the method of Kalman filter（KF） and extended Kalman filter（EKF） are illuminated. The status model and the measurement model of the passive location system are established. The procedure and formulas are deduced, which the measurement model was linearization and the position of the emitter was estimated by EKF. Through computer simulation, the method of passive location is validated feasibility, which is realized by EKF algorithm based on azimuth angle and its changing rate information. The result shows that the error of the estimate initialization affects the property of the location convergence.