CAJ | 학술논문

针对非线性非高斯的目标跟踪，传统的卡尔曼滤波和扩展卡尔曼滤波等算法将会出现滤波精度下降甚至发散的现象，提出了采用粒子滤波算法来解决非线性滤波问题；粒子滤波方法作为一种基于贝叶斯估计的非线性滤波算法，在处理非高斯非线性时变系统的参数估计和状态滤波问题方面有独到的优势，但是存在运算量大和实时性差的问题，因此提出了基于EKF的扩展粒子滤波；仿真结果表明：在强非线性非高斯环境下，PF算法的跟踪性能优于EKF算法，基于EKF的扩展粒子滤波能够取得较好的跟踪精度，并且能够有效的减少粒子滤波的运算量。
침대비선성비고사적목표근종，전통적잡이만려파화확전잡이만려파등산법장회출현려파정도하강심지발산적현상，제출료채용입자려파산법래해결비선성려파문제；입자려파방법작위일충기우패협사고계적비선성려파산법，재처리비고사비선성시변계통적삼수고계화상태려파문제방면유독도적우세，단시존재운산량대화실시성차적문제，인차제출료기우EKF적확전입자려파；방진결과표명：재강비선성비고사배경하，PF산법적근종성능우우EKF산법，기우EKF적확전입자려파능구취득교호적근종정도，병차능구유효적감소입자려파적운산량。
Particle Filter is presented to solve the nonlinear filter and non-Gaussian problem,while the al-gorithms of Kalman Filter and Extended Kalman Filter within the Gaussian background leads to the filter precision decrease and divergence phenomenon. As a nonlinear filter algorithm based on Bayesian estima-tion,particle filter has original advantage at treating the parameter estimation and state filtering aspects of nonlinear non-Gaussian time-varying systems,but it takes a lot of time due to larger number of particles. Thereby Extended Kalman Particle Filter is presented to solve the lower the real-time performance resulting from high computational complexity. The simulation results show that the PF approach outperforms the EKF algorithm under strong nonlinear and non-Gaussian environment,and EKPF gives better performance than EKF in solving high computation complexity.