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Optimal Parameter Design of Coarse Alignment for Fiber Optic Gyro Inertial Navigation System.

Lu B, Wang Q, Yu C, Gao W - Sensors (Basel) (2015)

Bottom Line: In previous works, integration time is selected by experience.The design process is accomplished based on the analysis of the error characteristics of these two coarse alignment algorithms.The analysis and simulation results show that the parameter provided by this work is the optimal value, and indicate that in different operational conditions, the coarse alignment algorithms adopted for FOG INS are different in order to achieve better performance.

View Article: PubMed Central - PubMed

Affiliation: College of Automation, Harbin Engineering University, Harbin 150001, China. lu_bao_feng@163.com.

ABSTRACT
Two different coarse alignment algorithms for Fiber Optic Gyro (FOG) Inertial Navigation System (INS) based on inertial reference frame are discussed in this paper. Both of them are based on gravity vector integration, therefore, the performance of these algorithms is determined by integration time. In previous works, integration time is selected by experience. In order to give a criterion for the selection process, and make the selection of the integration time more accurate, optimal parameter design of these algorithms for FOG INS is performed in this paper. The design process is accomplished based on the analysis of the error characteristics of these two coarse alignment algorithms. Moreover, this analysis and optimal parameter design allow us to make an adequate selection of the most accurate algorithm for FOG INS according to the actual operational conditions. The analysis and simulation results show that the parameter provided by this work is the optimal value, and indicate that in different operational conditions, the coarse alignment algorithms adopted for FOG INS are different in order to achieve better performance. Lastly, the experiment results validate the effectiveness of the proposed algorithm.

No MeSH data available.


Attitude errors of the 50 simulations for Algorithm 2.
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sensors-15-15006-f005: Attitude errors of the 50 simulations for Algorithm 2.

Mentions: To test the optimal parameters derived in Section 5.3.3, 50 simulations were performed based on the data of the 120 s test for each of the coarse alignment algorithms. Three different parameters were tested in each simulation, and the parameters were set as: . For Algorithm 1, in order to test the parameters, random velocity variation was introduced into the calculation, and it was modeled as a zero-mean white Gaussian noise of standard deviation 0.02 m/s. Similarly, for Algorithm 2, random position variation was introduced into the calculation, and it was modeled as a zero-mean white Gaussian noise of standard deviation 0.1 m. The simulation results of Algorithm 1 are shown in Figure 4, and their statistics are summarized in Table 12. The results of Algorithm 2 are presented in Figure 5, and their statistics are summarized in Table 13.


Optimal Parameter Design of Coarse Alignment for Fiber Optic Gyro Inertial Navigation System.

Lu B, Wang Q, Yu C, Gao W - Sensors (Basel) (2015)

Attitude errors of the 50 simulations for Algorithm 2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541819&req=5

sensors-15-15006-f005: Attitude errors of the 50 simulations for Algorithm 2.
Mentions: To test the optimal parameters derived in Section 5.3.3, 50 simulations were performed based on the data of the 120 s test for each of the coarse alignment algorithms. Three different parameters were tested in each simulation, and the parameters were set as: . For Algorithm 1, in order to test the parameters, random velocity variation was introduced into the calculation, and it was modeled as a zero-mean white Gaussian noise of standard deviation 0.02 m/s. Similarly, for Algorithm 2, random position variation was introduced into the calculation, and it was modeled as a zero-mean white Gaussian noise of standard deviation 0.1 m. The simulation results of Algorithm 1 are shown in Figure 4, and their statistics are summarized in Table 12. The results of Algorithm 2 are presented in Figure 5, and their statistics are summarized in Table 13.

Bottom Line: In previous works, integration time is selected by experience.The design process is accomplished based on the analysis of the error characteristics of these two coarse alignment algorithms.The analysis and simulation results show that the parameter provided by this work is the optimal value, and indicate that in different operational conditions, the coarse alignment algorithms adopted for FOG INS are different in order to achieve better performance.

View Article: PubMed Central - PubMed

Affiliation: College of Automation, Harbin Engineering University, Harbin 150001, China. lu_bao_feng@163.com.

ABSTRACT
Two different coarse alignment algorithms for Fiber Optic Gyro (FOG) Inertial Navigation System (INS) based on inertial reference frame are discussed in this paper. Both of them are based on gravity vector integration, therefore, the performance of these algorithms is determined by integration time. In previous works, integration time is selected by experience. In order to give a criterion for the selection process, and make the selection of the integration time more accurate, optimal parameter design of these algorithms for FOG INS is performed in this paper. The design process is accomplished based on the analysis of the error characteristics of these two coarse alignment algorithms. Moreover, this analysis and optimal parameter design allow us to make an adequate selection of the most accurate algorithm for FOG INS according to the actual operational conditions. The analysis and simulation results show that the parameter provided by this work is the optimal value, and indicate that in different operational conditions, the coarse alignment algorithms adopted for FOG INS are different in order to achieve better performance. Lastly, the experiment results validate the effectiveness of the proposed algorithm.

No MeSH data available.