Time Of Initial Alignment Research Articles

Comparison of Treatment Time of Initial Alignment Between Self Ligating and Conventional System

Comparison of Treatment Time of Initial Alignment Between Self Ligating and Conventional System

진동 외란 완화를 위한 교차상관 기반 초기 신속 개략 정렬 알고리즘

In this paper, we propose a rapid coarse alignment algorithm for mitigating vibratory disturbances, via employment of cross-correlation. The proposed algorithm analyzes the frequency of vibration disturbance due to the engine of a helicopter using a cross-correlation technique to cope with variable frequencies, and determines the initial alignment time by grasping the period. While the existing methods, such as maintaining a long initial alignment time or applying a filter, can passively remove vibration noise, the proposed method can actively cancel the vibration noise by adjusting the data acquisition time required for the initial alignment. Through this method, the sinusoidal time varying vibration can be actively canceled in a short time, making rapid initial alignment possible. The proposed algorithm was verified through Monte Carlo simulation for several situations.

Research on Zero-speed Detector Based on INS / Wheel speed and Vehicle gear

The zero-speed detector is required for the correction of the positioning error and the initial static alignment of the autonomous vehicle. The zero-speed detector that only depends on the INS (Inertial Navigation System) has zero-speed misjudgment. The performance of the detector after the fusion of wheel speed has been improved, but the wheel speed is still not integrated in scenarios such as long periods of stationary, emergency stop and reverse. Because the wheel speed signal only outputs the vehicle speed when the vehicle reaches a certain speed, there is a long initial alignment time, and the wheel speed is not fused in the complex driving process. This paper proposes a Vehicle gear information added to the INS / wheel speed fusion zero speed detector to achieve a 10s increase in the initial alignment time, the effect of wheel speed fusion has been improved, and thus the zero speed The accuracy of detection.

A Self-Alignment Method of MEMS Biaxial Rotation Modulation Strapdown Compass for Marine Applications

Self-alignment of strapdown inertial navigation systems incorporating micro-electro-mechanical systems (MSINS) is a great challenge for marine applications. In this paper, a self-alignment method for a rotating MEMS strapdown compass is proposed with the aim of solving this problem. First, based on an analysis of biaxial rotation modulation and initial alignment of the strapdown compass, a self-alignment method is presented and verified. Second, by analyzing the effects of biaxial rotation modulation, the proposed method is improved by speeding up the rotation and reducing the stop time of the biaxial rotation mechanism to shorten the initial alignment time, which effectively suppresses the influence of MEMS noise on the initial alignment error angle. The influence of ship swinging on the initial alignment error angle is also analyzed. The efficiency of the method is verified by experiments on a swinging base. Finally, a parameter adjustment approach is presented that allows the proposed method to be used with different types of MEMS. This approach is validated by experiments. All the experimental results demonstrate the efficiency and precision of the proposed method.

A modified rapid alignment method of SINS based on measurement augmentation

The precision and convergence time of initial alignment are two most important indicators for inertial navigation system, but they are usually contradictory. It is necessary to achieve considerable accuracy in a very short period for the launching of weapons such as artillery, torpedo and so on. However, the conventional methods need several minutes to complete the initial alignment. In this case, a Kalman filter method based on measurement augmentation (MAKF) is proposed to speed up convergence, and theoretical analysis of observability verifies the feasibility of the proposed method. Furthermore, a large misalignment angle model and a dimensionality reduction Cubature Kalman Filter using measurement augmentation (MACKF) are also employed to solve the invalidity of the linear model under the large misalignment angle. Finally, simulations and turntable experiment results show that the proposed method can speed up the convergence substantially. Even with initial misalignment angle of 70°, the azimuth error angle can be reduced within 1° in 5 s and 0.5° in 10 s. It has important engineering application value.

An Adaptive Initial Alignment Algorithm Based on Variance Component Estimation for a Strapdown Inertial Navigation System for AUV

As a typical navigation system, the strapdown inertial navigation system (SINS) is crucial for autonomous underwater vehicles (AUVs) since the SINS accuracy determines the performance of AUVs. Initial alignment is one of the key technologies in SINS, and initial alignment time and initial alignment accuracy affect the performance of SINS directly. As actual systems are nonlinear, the nonlinear filter is widely used to improve the accuracy of the initial alignment. Due to its higher precision and lower computational load, the cubature Kalman filter (CKF) has done well in state estimation. However, the noise characteristics need to be known exactly as prior knowledge, which is difficult or even impossible to achieve. Thus, the adaptive filter should be introduced in the initial alignment algorithm to suppress the uncertainty effect caused by the unknown system noise. Therefore, taking the nonlinearity and uncertainty into account, a novel initial alignment algorithm for AUVs is proposed in this manuscript, based on CKF and the adaptive variance components estimation (VCE) filter (VCKF). Additionally, the simulation and experiment results show that not only the accuracy, but also the convergence speed can be improved with this proposed method. The validity and superiority of this novel adaptive initial alignment algorithm based on VCKF are verified.

Parameter Identification Method for SINS Initial Alignment under Inertial Frame

The performance of a strapdown inertial navigation system (SINS) largely depends on the accuracy and rapidness of the initial alignment. The conventional alignment method with parameter identification has been already applied widely, but it needs to calculate the gyroscope drifts through two-position method; then the time of initial alignment is greatly prolonged. For this issue, a novel self-alignment algorithm by parameter identification method under inertial frame for SINS is proposed in this paper. Firstly, this coarse alignment method using the gravity in the inertial frame as a reference is discussed to overcome the limit of dynamic disturbance on a rocking base and fulfill the requirement for the fine alignment. Secondly, the fine alignment method by parameter identification under inertial frame is formulated. The theoretical analysis results show that the fine alignment model is fully self-aligned with no external reference information and the gyrodrifts can be estimated in real time. The simulation results demonstrate that the proposed method can achieve rapid and highly accurate initial alignment for SINS.

항공기 Run-Up 진동 환경에서의 관성항법장치 초기 정렬 방법 설계 및 평가

Abstract: Inertial Navigation Systems (INS) are widely used as the main navigation device for aircraft. To get the initial attitude, the INS requires the initial alignment before navigation starts. An aircraft also needs an engine test procedure that causes some vibrations before flight. An INS can’t be aligned in a vibration environment so the initial alignment is performed before the aircraft engine test. Therefore, the initial alignment time of an INS has been a major factor in limiting an aircraft’s takeoff response time. In this paper, we designed an initial alignment algorithm that can be executed even in disturbances such as aircraft run-up. We demonstrated verification of the algorithm that is embedded on the real INS and testing methods to evaluate the alignment of the INS. We also analyzed the test results of the proposed initial alignment algorithm that is performed during a real aircraft run-up. Keywords: strapdown inertial navigation system, initial alignment, unscented Kalman filter, heading attitude error

Periodic Error Compensation for Quartz MEMS Gyroscope Drift of INS

In order to improve the navigation accuracy of an inertial navigation system (INS), composed of quartz gyroscopes, the existing real-time compensation methods for periodic errors in quartz gyroscope drift and the periodic error term relationship between sampled original data and smoothed data are reviewed. On the base of the results, a new compensation method called using former period characteristics to compensate latter smoothness data (UFCL for short) method is proposed considering the INS working characteristics. This new method uses the original data without smoothing to work out an error conversion formula at the INS initial alignment time and then compensate the smoothed data errors by way of the formula at the navigation time. Both theoretical analysis and experimental results demonstrate that this method is able to cut down on computational time and raise the accuracy which makes it a better real-time compensation approach for periodic error terms of quartz micro electronic mechanical system (MEMS) gyroscope's zero drift.

A fast initial alignment method for strapdown inertial navigation system on stationary base

In this paper a strapdown inertial navigation system (SINS) error model is introduced, and the observability of the SINS error model is analyzed. Then, on the basis of this SINS error model and the analysis of computer simulation results, a fast estimation method of the azimuth error /spl Phi//sub D/ is proposed for the initial alignment of SINS on stationary base. Consequently, the time of initial alignment is reduced greatly. The computer simulation results illustrate the efficiency of this fast initial alignment method.