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AUV integrated navigation method and system based on M estimation

An integrated navigation system and integrated navigation technology, applied in the field of AUV integrated navigation based on M estimation, can solve the problems of state estimation accuracy decline, cooperative navigation algorithm positioning error divergence, etc.

Pending Publication Date: 2020-10-27
CHINA ACAD OF AEROSPACE AERODYNAMICS
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Problems solved by technology

[0005] The standard Kalman filter is the optimal solution for the Gaussian distribution of the measurement noise in the integrated navigation system. However, when the AUV is operating underwater, the abnormal clutter will have a great impact on the measurement information, and in many cases the noise appears Thick-tailed distribution characteristics will inevitably lead to a decrease in the accuracy of state estimation. In severe cases, it may cause the positioning error of the cooperative navigation algorithm to diverge.

Method used

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  • AUV integrated navigation method and system based on M estimation
  • AUV integrated navigation method and system based on M estimation
  • AUV integrated navigation method and system based on M estimation

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Embodiment 1

[0231] To verify the effectiveness of the M estimation algorithm under different conditions of interference factors, set the specific parameters of SINS in the integrated navigation system as accelerometer zero bias and gyro drift to be 5×10 -4 g,0.1° / h, the noise is 10 -6 g, 0.001° / h, the measurement error of USBL is 0.5% slant distance, the measurement noise of USBL is non-Gaussian, that is, it obeys the mixed Gaussian distribution, and compares the filtering of M estimation filter and standard Kalman filter under different conditions of interference factors in the mixed Gaussian distribution Effect. Here the interference factor is equal to 0.05, the standard deviation of interference distribution is equal to 100, and the simulation time is 3600s. The position error, velocity error, and attitude error of the SINS / USBL integrated navigation system based on the measurement noise mixed Gaussian distribution are simulated and verified. The change curve is as follows: image 3 ,...

Embodiment 2

[0235] To verify the validity of the M estimation algorithm under different conditions of standard deviation, this section analyzes and compares the M estimation filter with the standard Kalman filter under the condition that the measurement noise obeys the mixed Gaussian model with different standard deviations. Compared with the mixed Gaussian distribution in 3.2 where the interference factor is equal to 0.35 and the standard deviation is equal to 100, the control variable method is used here, the fixed interference factor is equal to 0.35, and the standard deviation is set to 50. The simulation time is 3600s. The position error, velocity error, and attitude error of the SINS / USBL integrated navigation system based on the mixed Gaussian distribution of measurement noise are simulated and verified. The change curves are as follows: Figure 9 , Figure 10 , Figure 11 shown. The specific navigation parameters obtained from the above simulation are used for RMS value statisti...

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Abstract

The invention discloses an AUV integrated navigation method and system based on M estimation, and provides a filtering algorithm based on generalized maximum likelihood estimation (M estimation); under the condition that system measurement noise is in non-Gaussian distribution, especially in the case of Gaussian mixture distribution (Gaussian nearby symmetric interference), an M-estimation algorithm is applied, measurement residual errors and state prediction residual errors are weighted through an influence function and a weight function, and the influence of measurement abnormal peak valueson a navigation system is reduced. The algorithm is applied to an SINS / USBL integrated navigation system; under the conditions of non-Gaussian measurement noise and abnormal measurement of the underwater acoustic sensor due to the multipath effect, by comparing the standard Kalman filtering algorithm with the M estimation filtering algorithm, the position and speed error precision of the M estimation filtering algorithm are obviously improved relative to those of the standard Kalman filtering algorithm, and under the condition that the Gaussian mixture model is severely polluted, the filteringeffect of the SINS / USBL integrated navigation system based on M estimation filtering is more obvious, and the robustness and the anti-interference performance are relatively better.

Description

technical field [0001] The invention relates to a method and system for AUV integrated navigation based on M estimation, belonging to the technical field of integrated navigation. Background technique [0002] Underwater vehicles (AUVs) are used as equipment for developing and exploring marine resources, and a variety of underwater vehicles can replace divers for underwater resource exploration and deep-sea operations. Obtaining accurate location information of underwater vehicles is the basic guarantee for underwater vehicles to perform operations. At this stage, the high-precision navigation and positioning technology in the harsh underwater environment has become a difficult problem that the development of autonomous underwater vehicles has to face. [0003] A single sensor cannot accurately provide the navigation and positioning parameters of the carrier. The integrated navigation system uses two or more sensors with complementary advantages to achieve a better navigati...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/16G01C21/20
CPCG01C21/165G01C21/20G01C21/005
Inventor 卢曼曼张南南李宇航郭眀禹
Owner CHINA ACAD OF AEROSPACE AERODYNAMICS
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