基于序列二次规划的非线性不等式状态约束滤波算法  

Nonlinear Inequality State Constrained Filtering Algorithm Based on Sequential Quadratic Programming

作　　者：陈金广[1,2] 贺姗[1] 马丽丽[1] 

机构地区：[1]西安工程大学计算机科学学院,西安710048 [2]西安电子科技大学电子工程学院,西安710071

出　　处：《计算机工程》2015年第5期295-299,共5页Computer Engineering

基　　金：国家自然科学基金资助项目(61201118);中国博士后科学基金资助项目(2103M532020);陕西省教育厅科研计划基金资助项目(14JK1304);西安工程大学学科建设经费基金资助项目

摘　　要：针对非线性不等式状态约束滤波问题,提出一种基于序列二次规划的迭代不敏卡尔曼滤波算法。在迭代不敏卡尔曼滤波的基础上,采用序列二次规划优化法求解非线性不等式约束条件下的最优解。通过对每一次迭代求解二次规划子问题来确定下降方向,重复该步骤直到求得原问题的解,利用效益函数对目标函数最小化和不等式约束条件进行权衡,以保证算法的收敛性,利用正定矩阵近似海森矩阵降低时间复杂度。对具有约束的航路跟踪系统进行实验仿真,结果表明,该算法在处理非线性不等式状态约束滤波问题时,能够有效地提高状态估计精度,获得较高的滤波精度,且时间复杂度较低。Aiming at the problem of nonlinear inequality filtering with state constraints,this paper presents an unscented iterated Kalman filtering algorithm based on sequential quadratic programming optimization method. The method uses sequential quadratic programming method to solve the optimum nonlinear inequality constrained problem. In iterations, quadratic programming sub-problems are employed to determine a descent direction,and these steps are repeated until the solution of original problem is obtained. In order to guarantee the convergence of the algorithm,it balances between the objective function and the inequality constraints. Furthermore, a positive definite matrix is used to approximate the Hessian matrix to reduce the complexity. A constrained tracking simulation is performed and the results show that the new algorithm can effectively enhance the accuracy with a low time complexity.

关 键 词：状态约束 迭代不敏卡尔曼滤波 优化算法 目标跟踪 信息融合 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]