Application of reverse processing in SINS initial alignment and SINS/GPS integrated navigation

Abstract

Purpose The study aims to propose reverse processing solution to improve the performance of strapdown inertial navigation system (SINS) initial alignment and SINS-/global positioning system- (GPS) integrated navigation. The proposed scheme can be well applied in the fields of aircraft and aerospace navigation. Design/methodology/approach For the SINS alignment phase, a fast initial alignment scheme is proposed: the initial value of reverse filter is determined by the final result of forward filter, and then, the reverse filter is carried out using the stored data. Multiple iterations are performed until the accuracy is satisfied. For the SINS-/GPS-integrated phase, a forward–reverse navigation algorithm is proposed: first, the standard forward filter is used, and then, the reverse filter is carried out using the initial value determined by the forward filter, and the final fusion results are achieved by the weighted smoothing of the forward and reverse filtering results. Findings The simulation and the actual test results show that in the initial alignment stage, the proposed reverse processing method can obviously shorten the SINS alignment time and improve the alignment accuracy. In the SINS-/GPS-integrated navigation data fusion stage, the proposed forward–reverse data fusion processing can, obviously, improve the performance of the navigation solution. Practical implications The proposed reverse processing technology has an important application in improving the accuracy of navigation and evaluating the performance of real-time navigation. The proposed scheme can be not only used for SINS-/GPS-integrated system but also applied to other integrated systems for general aviation aircraft. Originality/value Compared with the common forward filtering algorithm, the proposed reverse scheme can not only shorten alignment time and improve alignment accuracy but also improve the performance of the integrated navigation.

Filtering a Signal From Noise at a Sins Coarse Initial Alignment

“…Let's consider the application of the Kalman filter for filtering the signals of the SINS sensors. We will obtain averaging Kalman filters with forward flow [2] and with backward flow calculations, based on the provisions set forth in [10,11,12,13,14]. Will represent the linear model of the system under study in matrix form…”

“…The noise reduction task can be accomplished by applying the averaging Kalman filter (forward filter), as well as the Kalman filter in reverse time (backward filter) [9,10,11,12,13]. Expressions of the suboptimal backward filter practically coincide with the equations of the forward Kalman filter [9].…”

An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer

“…SINS can track the position and orientation of the movement of the carrier in real time based on the accelerometer and gyroscope inherent in SINS. The process of initial alignment prior to the navigation operation is of vital importance for SINS, the accuracy of subsequent dead-reckoning algorithm relies heavily on the initial attitude matrix, and a poor initial attitude matrix will result in much degradation and divergence for subsequent navigation [9,10].…”