Navigation data filtering method, device, computer equipment and storage medium

Abstract

The invention relates to a navigation data filtering method, a navigation data filtering apparatus, a computer device and a storage medium. The method comprises the following steps: establishing and initializing an inertial system parameter error model, and setting the time serial number of a next filtering cycle as an assigned value; obtaining a state quantity predication value and a state quantity covariance prediction value with the time serial number being the assigned value; obtaining a measured value estimation value and a measured value covariance estimation value with the time serial number being the assigned value and a state quantity and measured value cross-covariance; calculating a Gaussian kernel coefficient; obtaining a state quantity posterior estimation value with the timeserial number being the assigned value; updating the state quantity covariance with the time serial number being the assigned value; compensating a parameter; and setting an assigned value to be valueof (the assigned value +1), and returning to the obtaining of the state quantity prediction value with the time serial number being the assigned value and the state covariance prediction value with the time serial number being the assigned value. The method in the embodiment of the invention is enforced to realize the capture of non-Gaussian noise high-order terms, improve the error estimation accuracy and improve the positioning precision.

Description

technical field [0001] The invention relates to a vehicle integrated navigation system, and more specifically refers to a navigation data filtering method, device, computer equipment and storage medium. Background technique [0002] The theoretical basis of the inertial system is Newton's law of mechanics. The operation of the system only needs the fusion of acceleration and angular velocity information, does not need to receive other auxiliary information, and does not generate outward radiation. It has the characteristics of shielding and no interference, and can Provide high-precision relative position information. Since entering the 21st century, inertial sensors based on MEMS (Micro-Electro-Mechanical System) technology have been successfully used in the consumer market. However, the application of inertial sensors to navigation systems needs to solve the problem of rapid accumulation of errors in the calculation process. [0003] In the context of the coexistence of ...

AI Technical Summary

Problems solved by technology

Since the subsystem has its own errors, the development of an integrated navigation system needs to fuse the data of the two subsystems and compensate for the errors. The most commonly used Kalman filter algorithm and various improved algorithms are based on the minimum mean square error criterion. , this type of method can achieve the optimal filtering performance for Gaussian noise, but cannot capture the high-order terms of non-Gaussian noise. In addition, it can also use non-Kalman filter iterative filtering algorithm for data fusion and error compensation, such as particle Both filtering and neural network filtering algorithms require a large amount of calculation, and are not suitable for integrated navigation systems with high state dimension dimensions

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