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Filtering model real-time correction method and system for integrated positioning

A combined positioning and real-time correction technology, applied in the field of navigation, can solve problems such as difficulty in balancing the deviation of dynamic models and observation models, inaccurate prior knowledge, and large computational burden for high-dimensional systems.

Active Publication Date: 2019-05-31
LANZHOU JIAOTONG UNIV +1
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Problems solved by technology

However, the first method is often difficult to balance the deviation between the dynamic model and the observation model due to inaccurate prior knowledge; the second method will bear a huge computational burden for high-dimensional systems

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  • Filtering model real-time correction method and system for integrated positioning
  • Filtering model real-time correction method and system for integrated positioning
  • Filtering model real-time correction method and system for integrated positioning

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Embodiment Construction

[0106] The method for real-time correction of the filter model in combined positioning provided by the embodiment of the present invention includes:

[0107] Step (1), extended Kalman filter:

[0108] Obtain data for a stochastic system of interest and construct a nonlinear model:

[0109] x k =f(X k-1 )+Γ k-1 W K-1 (1)

[0110] Z k =h(X k )+V k (2)

[0111] In the above formula, X k ∈ R n is the state variable at time k, Z k ∈ R nis the measured variable at time k; W is the process noise of zero-mean normal distribution with variance Q, and V is also V k ~N(0,R k ) observation noise; f() and h() are two nonlinear functions;

[0112] The linearized and simplified models of equations (1) and (2) are as follows:

[0113] x k = Φ k,k-1 x k-1 +M k-1 +Γ k-1 W k-1 (3)

[0114] Z k =H k x k +N k +V k (4)

[0115] The general EKF algorithm is implemented recursively in the following set of fundamental equations:

[0116] (a) State one-step prediction ...

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Abstract

The present invention provides a filtering model real-time correction method and system for integrated positioning. The method includes the following steps that: step (1) the data of a target stochastic system are obtained, and extended Kalman filter processing is performed; step (2) dynamic model deviations are calculated; (3) dynamic model deviation training is performed with an LSSVM; and step(4) unscented transformation is carried out. According to the method and system of the invention, the LSSVM (least squares support vector machine) is utilized to improve an EKF algorithm, and the improved algorithm is applied to the integrated positioning estimation of vehicles; the LSSVM and the EKF are combined through the unscented transformation (UT), a time-varying function is constructed with a fuzzy set; it is considered that deviations are in Gaussian normal distribution, and training is performed with a finite data set through the LSSVM; deviation estimation is performed through the deviation values of historical information; and therefore, the deviations are corrected and compensated.

Description

technical field [0001] The invention relates to the technical field of navigation, in particular to a method and system for real-time correction of a filter model in combined positioning. Background technique [0002] my country is gradually entering the era of intelligent transportation. The precise positioning of vehicles is not only related to the safety of vehicles, but also directly affects the efficiency of vehicle dispatching. Therefore, the study of continuous, high-precision, low-cost, and reliable vehicle positioning has become a key scientific and technological problem that needs to be solved urgently in the field of intelligent transportation in my country. [0003] Kalman filter (KF) is a high-efficiency recursive filter (autoregressive filter), which can estimate the state of a dynamic system from a series of measurements that do not completely contain noise. However, simple Kalman filtering must be applied in in a system with a Gaussian distribution. Therefo...

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Application Information

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IPC IPC(8): G01C21/28G01C25/00G01S19/49
Inventor 陈光武刘昊杨菊花程鉴皓
Owner LANZHOU JIAOTONG UNIV
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