Coarse Alignment for SINS Using Gravity in the Inertial Frame Based on Attitude Quaternion

In this paper, we proposed a coarse-alignment method for strapdown inertial navigation systems based on attitude determination. The observation vectors, which can be obtained by inertial sensors, usually contain various types of noise, which affects the convergence rate and the accuracy of the coarse alignment. Given this drawback, we studied an attitude-determination method named optimal-REQUEST, which is an optimal method for attitude determination that is based on observation vectors. Compared to the traditional attitude-determination method, the filtering gain of the proposed method is tuned autonomously; thus, the convergence rate of the attitude determination is faster than in the traditional method. Within the proposed method, we developed an iterative method for determining the attitude quaternion. We carried out simulation and turntable tests, which we used to validate the proposed method’s performance. The experiment’s results showed that the convergence rate of the proposed optimal-REQUEST algorithm is faster and that the coarse alignment’s stability is higher. In summary, the proposed method has a high applicability to practical systems.

Section: Introductionmentioning

confidence: 99%

A Coarse-Alignment Method Based on the Optimal-REQUEST Algorithm

Zhu

Zhang

2018

“…Coarse alignment is important since it provides a rapidly alignment result for the fine alignment. The existing algorithms of the coarse alignment mainly include: analytic coarse alignment [7,8,9], inertial frame coarse alignment (IFCA) [10,11], and coarse alignment based on Davenport’s q method [12,13,14,15,16]. The analytic coarse alignment can only be used on static base.…”

Section: Introductionmentioning

confidence: 99%

An Improved Coarse Alignment Algorithm for Odometer-Aided SINS Based on the Optimization Design Method

Zhang

Luo

Fang

et al. 2018

An improved coarse alignment (ICA) algorithm is proposed in this paper with a focus on improving alignment accuracy of odometer-aided strapdown inertial navigation system (SINS) under variable velocity and variable acceleration condition. In the proposed algorithm, the outputs of inertial sensors and odometer in a sampling interval are linearized rather than assumed to be a constant, which improves the accuracy of the vector observations and the precision of coarse alignment. Simulation and field test results illustrate that, under variable velocity and variable acceleration condition, the proposed algorithm can obtain a better alignment performance than conventional coarse alignment method.

“…The objective of a coarse alignment is to provide a good condition for a fine alignment, where the misalignment angles are approximately estimated to a few degrees. In the fine alignment stage, the small misalignment angles are computed accurately and the precise initial transformation matrix is then formulated [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Alignment Method for the Strapdown Inertial Navigation System (SINS)

Gao

et al. 2016

In this paper, an innovative inertial navigation system (INS) mechanization and the associated Kalman filter (KF) are developed to implement a fine alignment for the strapdown INS (SINS) on stationary base. The improved mechanization is established in the pseudo-geographic frame, which is rebuilt based on the initial position. The new mechanization eliminates the effects of linear movement errors on the heading by decoupling. Compared with the traditional local-level mechanization, it has more advantages. The proposed algorithm requires lower coarse alignment accuracy in both the open-loop and closed-loop KFs and hence can improve the system reliability and decrease the total alignment time. Moreover, for the closed-loop KF, it can decrease oscillation caused by the system errors and improve the closed-loop system stability. In addition, the proposed algorithm can also be applied to polar alignment. The performance of the proposed algorithm is verified by both simulations and experiments and the results exhibit the superior performance of the proposed approach.