Adaptive Kalman filtering algorithm applied to GPS navigation

An adaptive Kalman and GPS navigation technology, applied in the field of GPS navigation, can solve problems such as waste of resources, difficulty in applying the algorithm directly to the GPS tracking system, and reduce the performance of the tracker, so as to achieve the effect of accurate motion state.

Inactive Publication Date: 2016-05-04
NORTHWEST A & F UNIV
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Problems solved by technology

The current single-model maneuvering target tracking algorithm cannot achieve adaptive tracking, while the multi-model maneuvering target tracking algorithm will have a lot of extra models when tracking weak maneuvering targets, which will not only

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  • Adaptive Kalman filtering algorithm applied to GPS navigation
  • Adaptive Kalman filtering algorithm applied to GPS navigation
  • Adaptive Kalman filtering algorithm applied to GPS navigation

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Embodiment Construction

[0028] The implementation of the present invention will be described in detail below in conjunction with the drawings and examples.

[0029] Such as figure 1 Shown, the present invention is a kind of adaptive Kalman filtering algorithm applied to GPS navigation, comprises the steps:

[0030] Step 1: Set the model parameters: the dimension M of the state variable, the order N of the polynomial, and satisfy M≥N+2, the initial matrix Q of the variance of the state noise 0 , and for the estimated mean x of the target state k-1|k-1 and covariance P k-1|k-1 Initialize, k represents the sampling point of discrete time; the initial state estimate x 0|0 =[z 0 z -1 …z -M+1 ] T , where z i (i=-M+1,-M+2,...,-1,0) are the target position observations at each moment before the filter starts;

[0031] Step 2 establishes the AR model, applies the Kalman filter framework, and calculates the AR model coefficients;

[0032] The specific implementation process of this step is as follows...

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Abstract

The invention discloses an adaptive Kalman filtering algorithm applied to GPS navigation and mainly solves a problem that an adaptive Kalman filtering algorithm in the prior art can not realizes adaptive adjustment in a filtering process. The algorithm comprises steps that (1), algorithm parameters are set, and initialization of a target state is carried out; (2), an AR model is established, and a coefficient of the AR model is calculated by utilizing a Kalman filtering framework; (3), the target state is predicted by utilizing Kalman filtering on the basis of the AR model, and a prediction mean value and an error covariance are included; (4), the measurement data is utilized to update the target state, and a gain matrix, a posteriori estimation mean value and a posteriori estimation error covariance are calculated; and (5),a state noise covariance is adaptively calculated online, a state value and a covariance of a target position are outputted, k is made to increase 1, and the process returns to the step (2). Compared with the adaptive Kalman filtering algorithm in the prior art, the method can realize more accurate target state estimation, and the method can be applied to practical GPS navigation systems.

Description

technical field [0001] The invention belongs to the technical field of GPS navigation, in particular to an adaptive Kalman filtering algorithm applied to GPS navigation, which can be used for data processing in the GPS navigation system. Background technique [0002] With the rapid development of precision agriculture, intelligent agriculture, mainly supported by advanced science and technology, has become the backbone of agricultural production. While greatly reducing the work intensity and injuries of farmers, it has also greatly improved work efficiency and labor productivity. However, to realize the precise operation of agricultural equipment, it mainly depends on the precise perception of the location of the farmland by the equipment. GPS (Globalpositioning System), as the preferred method of farmland location perception, has been widely used in modern agricultural production. Precision agriculture requires GPS to have higher tracking accuracy, more accurate target reco...

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Application Information

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IPC IPC(8): G01S19/42
CPCG01S19/42
Inventor 靳标郭交王胜兰苏宝峰朱学卫
Owner NORTHWEST A & F UNIV
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