Adaptive unscented Kalman particle filtering method

An unscented Kalman and particle filter technology, applied in navigation, instrumentation, surveying and navigation, etc., can solve problems such as the inability of the system to perceive, the poor state estimation effect of strongly nonlinear systems, and the deterioration of filtering results

Pending Publication Date: 2019-11-15
NANJING UNIV OF SCI & TECH
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Problems solved by technology

[0005] Most filtering methods are poor for state estimation of strongly nonlinear systems, and when the external noise changes...

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  • Adaptive unscented Kalman particle filtering method
  • Adaptive unscented Kalman particle filtering method
  • Adaptive unscented Kalman particle filtering method

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Embodiment Construction

[0097] The present invention will be described in further detail below.

[0098] An adaptive unscented Kalman particle filter method, which determines the recommended probability density function according to the unscented Kalman filter UKF, uses particle filter to estimate the state of the strong nonlinear system, and makes real-time estimation of the external noise, greatly reducing noise anomalies navigation results.

[0099] like figure 1 As shown, an adaptive unscented Kalman particle filter method, the steps are as follows:

[0100] Step 1. Carry out nonlinear system modeling for the inertial / satellite tight integrated navigation system, where the error amount X of the strapdown inertial navigation system is selected I and GPS error amount X G As the state quantity of the whole system, namely:

[0101]

[0102] in, are the attitude error angles in the three directions of the northeast sky, δV E , δV N , δV U are the velocity errors in the three directions of ...

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Abstract

The invention discloses an adaptive unscented Kalman particle filtering method. The method utilizes the theory of the Sage filtering windowing method, also combines the idea of fading, estimates a true covariance matrix of the observed quantity by collecting an epoch innovation vector, and compares the true covariance matrix with the covariance matrix of a filtering recursive model, when a deviation exists between the two covariances, the observed covariance matrix of the system is adaptively adjusted according to the difference. Based on the process, an adaptive fading factor is designed, theobservation noise is further modified, the modified observation noise participates in the solution of a gain matrix, and thus the state estimation can be adaptively adjusted. The scheme of the invention can effectively perform filtering correction on a strongly nonlinear satellite/inertial integrated navigation system, especially when the external noise is abnormal, the filtering gain can be effectively and adaptively adjusted to improve the robustness and positioning accuracy of the system.

Description

technical field [0001] The invention belongs to the field of nonlinear combined navigation, in particular to an adaptive unscented Kalman particle filter method. Background technique [0002] Conventional Kalman filtering is a model-based linear minimum variance estimation, but in actual engineering practice, the system is often nonlinear. Although the Extended Kalman Filter (EKF, Extended Kalman Filter) can solve the problem of nonlinear system state estimation, it performs Taylor expansion on the system equation, ignores the influence of high-order terms on the system, and has a certain truncation error. The Unscented Kalman Filter UKF (Unscented Kalman Filter) determines the optimal gain matrix based on the covariance of the estimated quantity and quantity measurement, and the covariance matrix is ​​calculated based on the recurring double σ sample point, and the sample point is calculated according to the system The nonlinear model calculation. So it can be used for bo...

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Application Information

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IPC IPC(8): G01C21/16G01C25/00
CPCG01C21/165G01C25/005G01C25/00
Inventor 陈帅温哲君刘善武谭聚豪王琛顾得友
Owner NANJING UNIV OF SCI & TECH
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