In many real navigation problems, moving objects may have some state or measurement constraints along their way. Using these constraints in conventional Extended Kalman Filter (EKF) equations results large matrices which are computationally time-consuming. In this paper, a new Constrained Navigation Filter (CNF) is proposed as a parallel to reduce the computational burden of the conventional EKF algorithm while increasing the positioning accuracy. So a methodology has been developed for Strap-down Inertial Navigation System (SINS) based on MEMS IMU applied on Pipeline Inspection Gauge (PIG) to sense data at constant sampling rate of 108 km of the pipeline. The results verified that using such a hybrid approach has improved positional accuracy 8.97% in comparison with that of the latest methods like EKF/ Pipe Line Junctions (PLJ). Also, the proposed method is 2.277 times better than EKF/PLJ in the algorithm runtime.

在许多实际的导航问题中，移动物体沿途可能有一些状态或测量约束。在传统的扩展卡尔曼滤波器 (EKF) 方程中使用这些约束会产生计算耗时的大型矩阵。本文提出了一种新的约束导航滤波器（CNF）作为并行，以减少传统EKF算法的计算负担，同时提高定位精度。因此，我们开发了一种基于 MEMS IMU 的捷联惯性导航系统 (SINS) 的方法，应用于管道检测仪 (PIG)，以 108 公里管道的恒定采样率感测数据。结果验证，与 EKF/管道连接点 (PLJ) 等最新方法相比，使用这种混合方法将位置精度提高了 8.97%。还，