Harbin Engineering University

Lei Wu

Yuxin Zhao

Tingxiao Wei

Yueyang Ben

Qian Li

Shuaiyang Li

Lianzhao Wang

Guangtao Zhou

Yu Guo

Bo Xu

Shengxin Li

Jiancheng Wang

Xinle Zang

Yifan Zhou

Junmiao Hu

Jiao Zhang

Chong Chen

Xiaoyu Wang

Kunming Jin

Zhaoyang Wang

Xiaofeng Wei

Dalong Zhang

Qianqian Gao

The disclosure belongs to the technical field of integrated navigation under non-ideal conditions, and in particular relates to a robust filtering method for integrated navigation based on a statistical similarity measure (SSM). In view of the situation that there are normal beam measurement information of the DVL and beam measurement information with a large error simultaneously in a SINS/DVL tightly integrated system, and aiming at the problem that the existing robust filters of an integrated navigation system process the measurement information in a rough manner and are likely to lead to loss of normal measurement information, the disclosure proposes a novel robust filtering method based on decomposition of multi-dimensional measurement equations and the SSM. The disclosure introduces the SSM theory while decomposing the multi-dimensional measurement equations of the SINS/DVL tightly integrated navigation system, and assists the measurement noise variance of each beam to complete respective adaptive update in case of a large measurement error, finally ensuring independence of processing of the measurement information of each beam. The disclosure can be used in the field of integrated navigation of underwater vehicles under non-ideal conditions.

Robust filtering method for integrated navigation based on statistical similarity measure

Disclosed is a self-adaptive horizontal attitude measurement method based on motion state monitoring. Based on a newly established state space model, a horizontal attitude angle is taken as a state variable, an angular velocity increment Δωfor compensating a random constant zero offset is taken as a control input of a system equation, and a specific force ffor compensating the random constant zero offset is taken as a measurement quantity. Meanwhile, judgment conditions for a maneuvering state of a carrier are improved, and maneuvering information of the carrier is judged by comprehensively utilizing acceleration information and angular velocity information output by a micro electro mechanical system inertial measurement unit (MEMS-IMU), whereby a measurement noise matrix of a filter can be automatically adjusted, thereby effectively reducing the influence of carrier maneuvering on the calculation of a horizontal attitude. The method has no special requirement on the maneuvering state of the carrier, and can ensure that the system has high attitude measurement precision in different motion states without an external information assistance.

Self-adaptive horizontal attitude measurement method based on motion state monitoring

A strapdown inertial navigation heave measurement method using multiple low-pass filter units includes: firstly, collecting data of a gyroscope and an accelerometer by a system, obtaining attitude information of a carrier by using initial alignment, and then, obtaining a relationship matrix between a body coordinate system and a geographic coordinate system by using the attitude information; obtaining a relationship matrix between the geographic coordinate system and a reference coordinate system according to a geographic position, and obtaining a rough vertical acceleration by using a direction cosine matrix, output information of the accelerometer and gravity information; then, filtering out low-frequency signals by using a first filter unit and integration to obtain a relatively accurate velocity signal; and furthermore, enabling the vertical acceleration to be subjected to a second filter unit and integration to obtain an accurate heave displacement.

Strapdown inertial navigation heave measurement method using multiple low-pass filter units

The invention provides a method for initial alignment of radar assisted airborne strapdown inertial navigation system. By calculating the slant distance and angular position between the radar and the airborne inertial navigation equipment, a nonlinear measurement equation for the initial alignment of the radar assisted inertial navigation system is obtained. The unscented Kalman filter algorithm is used to estimate and compensate the error amount of strapdown inertial navigation system to complete the initial alignment task. The significance of the present invention is to provide an in-flight initial alignment solution when the global positioning system is limited, which has fast convergence speed and high estimation accuracy and has high engineering application value.

Method for initial alignment of radar assisted airborne strapdown inertial navigation system

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Harbin, China

Lei Wu