Southeast University

Xiyuan Chen

Junwei Wang

Lin Fang

Ping Yang

Xuefen Zhu

Bingbo Cui

Xinhua Tang

Wei Dong Wang

Yang Yang

Xin Shao

Zhengyang Zhao

Xiaosu Xu

Xiying Wang

Xiaotian Zhang

Siyi Zhang

A navigation and positioning system for an underwater glider includes a micro-electro-mechanical system inertial measurement unit, a global positioning system receiving module, a triaxial magnetometer, a Doppler velocimeter, and an integrated navigation hardware processing system. If a navigation and positioning error is too large, the underwater glider stops working in real time and switches from the underwater working state to the up floating error correction state; and when velocity and location errors of a GPS/INS integrated navigation system are smaller than specified thresholds, the underwater glider switches from the up floating error correction state to the underwater working state and continues to work. An H∞ Kalman filter algorithm based on an adaptive multiple fading factor is established to ensure robustness and adaptability of navigation and positioning of a glider.

Navigation and positioning system for underwater glider and up floating error correction method

A dual-filter-based transfer alignment method under dynamic deformation. A dynamic deformation angle generated under dynamic deformation and a coupling angle between dynamic deformation and body motion will reduce the accuracy of transfer alignment; and a transfer alignment filter is divided into two parts, the first part estimates a bending deformation angle and the coupling angle, and uses an attitude matching method, and the second part estimates a dynamic lever arm, and uses a 'speed plus angular speed' matching method.

Dual-filter-based transfer alignment method under dynamic deformation

A motion constraint-aided underwater integrated navigation method employing improved Sage-Husa adaptive filtering includes establishing a Doppler log error model; constructing a state equation for an underwater integrated navigation system employing Kalman filtering; according to a relationship between a centripetal acceleration and a forward velocity of an underwater vehicle, establishing a constraint condition, and constructing a complete motion constraint model; establishing two measurement equations; and establishing a filter equation, conducting calculation by using a standard Kalman filtering algorithm when an underwater glider normally runs, and conducting time updating, measurement updating and filtering updating by using an improved Sage-Husa adaptive filtering algorithm when a measurement noise varies. The motion constraint-aided underwater integrated navigation method improves a filtering accuracy of the underwater integrated navigation system, restrains a filter divergence and has robustness and reliability.

Motion constraint-aided underwater integrated navigation method employing improved Sage-Husa adaptive filtering

A strict reverse navigation method for optimal estimation of fine alignment is provided. The strict reverse navigation method including: establishing an adaptive control function; performing a forward navigation calculation process; performing a reverse navigation calculation process; and performing the adaptive control for a number of forward and reverse calculations. The strict reverse navigation method shortens an alignment time for the optimal estimation of fine alignment while ensuring an alignment accuracy. The strict reverse navigation method provided effectively solves a problem that an error of an initial value of filtering in an initial stage of the optimal estimation of fine alignment affects convergence speeds of subsequent stages. In the initial stage, a larger number of the forward and reverse navigation calculations are adopted to reduce an error of the initial value as much as possible and increase a convergence speed of the filtering.

Strict reverse navigation method for optimal estimation of fine alignment

A method for decoupling an angular velocity in a transfer alignment process under a dynamic deformation includes: (1) generating, by a trajectory generator, information about an attitude, a velocity, and a position of a main inertial navigation system and an output of an inertial device, and simulating a bending deformation angle {right arrow over (&#x3b8;)} between the main inertial navigation system and a slave inertial navigation system and a bending deformation angular velocity {right arrow over (&#x3c9;)}by using second-order Markov; (2) decomposing the dynamic deformation into a vibration deformation and a bending deformation, and establishing an angular velocity model under the dynamic deformation of a wing; (3) deducing an error angle &#x394;{right arrow over (&#x3d5;)} between the main inertial navigation system and the slave inertial navigation system; and (4) deducing an expression &#x394;{right arrow over (&#x3c9;)} of a coupling error angular velocity, and applying that to an angular velocity matching process of transfer alignment to improve the precision of the transfer alignment.

Method for decoupling angular velocity in transfer alignment process under dynamic deformation

The present invention discloses a cubature Kalman filtering method suitable for high-dimensional GNSS/INS deep coupling, including: S, constructing a high-dimensional GNSS/INS deep coupling filter model; S, generating an initialization cubature point for the constructed filter model by using standard cubature rules; S, performing CKF filtering by using novel cubature point update rules. The present invention is suitable for high-dimensional GNSS/INS deep coupling filtering with high precision and high stability.

Cubature Kalman filtering method suitable for high-dimensional GNSS/INS deep coupling

The present invention discloses a joint non-coherent integral vector tracking method based on a spatial domain, which is used for further improving the performance of a vector tracking GPS (Global Positioning System) receiver. In a new vector tracking strategy design, a phase discriminator/a frequency discriminator in a traditional vector tracking loop is discarded, and baseband signals of visible satellites in each channel are taken as an observation value after performing non-coherent integration, and EKF (abbreviation of Extended Kalman Filter) is used to estimate directly and to solve the position, the velocity, a clock error, etc. of the GPS receiver. Because of the existence of non-coherent integral calculation, when GPS satellite signals are relatively weak, a carrier to noise ratio of an observation value may be effectively improved, and the tracking sensitivity is improved.

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Nanjing, China

Xiyuan Chen