# In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS
###### Abstract  
In this paper, an improved in-motion coarse alignment method is proposed for a strapdown inertial navigation system (SINS) using position loci obtained from the Global Positioning System (GPS). The difference from the popular coarse alignment methods is that the proposed algorithm uses GPS position loci information to form the vector observation, and does not need velocity information, which expands the application range of in-motion coarse alignment. In addition, this paper utilizes the Optimal-REQUEST algorithm to reduce the influence of random errors contained in the vector observation. The Optimal-REQUEST algorithm is an adaptive iterative updating algorithm, which can adaptively adjust the gain of the filter according to the loss function. Simulation results confirmed that the proposed algorithm can suppress the impact of random errors effectively. The pitch, roll and yaw angles calculated by the proposed algorithm were improved by 51.95%, 53.80% and 63.03% compared with the comparison algorithms. coarse alignment; strapdown inertial navigation system; attitude estimation; observation vector +
Footnote †: journal: Applied  
proposed in [15] is easily affected by random noise contained in accelerometer measurement. However, when the measurement time interval is very short, or the measurement quantity is inaccurate, the apparent motion of gravity will be collinear. In order to avoid collinearity among apparent motions, the authors of [16] propose an online sensor data denoising method and study the design of a novel reconstruction method for apparent motion Through the reconstruction of vectors, the method can increase the accuracy of self-alignment. In addition, in [17], a fast self-alignment method with real-time estimated latitude to obtain high accuracy with unknown geographic latitude constraints and limited time is proposed. Unfortunately, the above three coarse alignment methods can only be applied to the static base or swaying base and limit the application range of a SINS. Therefore, researchers have begun to investigate in-motion coarse alignment methods based on an external equipment auxiliary. When a vehicle is moving, the coarse alignment process cannot be completed using measurement information from inertial sensors alone, and it is necessary to engage the assistance of external equipment. GPS is the most popular auxiliary equipment for land vehicles, and many coarse alignment methods based on GPS have been studied in recent years. The authors of [18] investigated an optimization-based alignment (OBA) algorithm in an innovative way. The OBA algorithm uses infinite vector observations to transform the coarse alignment problem into an attitude determination problem. In [19], the approach reported in [18] is further developed by utilization of devised velocity integration formulae to construct the vector observation. Based on the above two algorithms, the basic problem of in-motion alignment can be solved. Unfortunately, both algorithms cannot suppress the impact of outliers contained in the outputs of GPS and inertial measurement units. Thus, in [20], a robust optimization-based alignment method to detect the outliers and improve the accuracy of in-motion coarse alignment is proposed. It is worth noting that the above in-motion coarse alignment methods are all based on the ground velocity. However, not all GPS receivers can export the ground velocity and some of them can only provide the position information of the vehicle. This limits the application of in-motion coarse alignment. Therefore, ref. [21] investigated an in-motion coarse alignment method for the SINS/GPS integrated system using position loci. The vector observation, based on position trajectory, further expands the application range of coarse alignment on the moving base. However, ref. [21] uses a filter-QUEST algorithm to calculate the attitude matrix, which has only limited ability to reduce the errors of vector observation contained in the inertial sensors and GPS measurement.