CAJ | 학술논문

该文针对GPS/DR(Global Positioning System/Dead-Reckoning)组合导航系统中存在参数不确定性问题,构建自组织状态空间模型,应用蒙特卡罗粒子滤波方法对新构建的系统模型进行滤波.并且对于在自组织模型中粒子滤波易使未知参数搜索陷入初始取样子集的问题,提出一种人工鱼群-粒子滤波算法.该算法不仅可以估计系统状态,而且还能使未知参数的取样分布向真实参数分布“移动”,最终辨识出未知参数的真值.仿真结果表明该方法的有效性.
해문침대GPS/DR(Global Positioning System/Dead-Reckoning)조합도항계통중존재삼수불학정성문제,구건자조직상태공간모형,응용몽특잡라입자려파방법대신구건적계통모형진행려파.병차대우재자조직모형중입자려파역사미지삼수수색함입초시취양자집적문제,제출일충인공어군-입자려파산법.해산법불부가이고계계통상태,이차환능사미지삼수적취양분포향진실삼수분포“이동”,최종변식출미지삼수적진치.방진결과표명해방법적유효성.
@@@@This paper propses a filtering method for GPS/DR (Global Positioning System/Dead-Reckoning) integrated navigation system with unknown parameters. This method firstly structures a self-organizing state space model, and then estimates the state vector by using Monte Carlo filtering method for this new system model. Because particle filter is easy to make a search of the unknown parameters into a subset of the initial sampling for the self-organization model an artificial fish swarm-partical filter algorithm is put forward. The algorithm not only can estimate the system state, but also can make the sampling distribution of the unknown parameters move to the true parameter distribution. Ultimately, the true value of the unknown parameters are identified. The simuliation results show the effectiveness of the proposed method.