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Adaptive Kalman noise estimation method and system based on data fusion

An adaptive Kalman and data fusion technology, applied in the field of noise estimation, can solve problems such as unknown second-order moment, and achieve the effect of improving accuracy and solving filtering problems.

Pending Publication Date: 2022-05-31
SHANDONG NORMAL UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the above problems, the present invention proposes an adaptive Kalman noise estimation method and system based on data fusion, and the present invention solves the filtering problem of unknown second-order moments in the noise statistical characteristics of the traditional Kalman model

Method used

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  • Adaptive Kalman noise estimation method and system based on data fusion
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  • Adaptive Kalman noise estimation method and system based on data fusion

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Embodiment 1

[0037] (1) Use multiple sensors to track the dynamic target, and obtain the observation value of each sensor. For example in a vehicle model

[0044] Consider the following model:

[0045]

[0047]

[0050]

[0051]

[0053] Based on equation (1), the process of the classical Kalman filter can be divided into two steps.

[0055]

[0056] P

[0059] K

[0060]

[0061] P

[0065]

[0067]

[0069]

[0070] As long as the variance is as small as possible, the value is closer to the true value, then:

[0071]

[0073]

[0075]

[0080] Z

[0081] Suppose there are i sensors, where (1≤s≤i):

[0082]

[0083] is the t-th component (1≤t≤m) measured by the s-th sensor at time k.

[0085]

[0087]

[0090] When p=1; represents the p-th component after the v-th data fusion.

[0093] The following assumptions and lemmas are given first.

[0096] Theorem 1: Under the premise of Assumption 2, the variance of F can be estimated as:

[0097]

[0099]

[0101]

[0104]

[0105] Therefore, in ord...

Embodiment 2

[0165] By analogy, the observation data fusion of multiple sensors is completed to obtain the fusion observations of multiple sensors;

[0169] The proposed system can be implemented in other ways. For example, the system embodiments described above are only

Embodiment 3

[0171] This embodiment provides a computer-readable storage medium on which a computer program is stored, the program being processed

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Abstract

The invention relates to an adaptive Kalman noise estimation method and system based on data fusion, and the method comprises the steps: tracking a dynamic target through a plurality of sensors, and obtaining an observation value of each sensor; performing first data fusion based on the observation values of the first two sensors to obtain first fusion data; performing second data fusion on the first fusion data and the observation value of the next sensor to obtain second fusion data; by parity of reasoning, observation value data fusion of the multiple sensors is completed, and fusion observation values of the multiple sensors are obtained; and obtaining a process noise covariance matrix estimation value and a measurement noise covariance estimation value based on the fusion observation values of the plurality of sensors. According to the invention, data fusion is carried out on the measurement sequences obtained by the plurality of sensors to improve the accuracy of the measurement data, and the filtering problem of unknown second moment in the Kalman model noise statistical characteristics is solved.

Description

An adaptive Kalman noise estimation method and system based on data fusion technical field The invention belongs to the technical field of noise estimation, be specifically related to a kind of adaptive Kalman noise based on data fusion Estimation methods and systems. Background technique The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art information. technique. [0003] With the rapid development of advanced navigation technology and the mass deployment of low-cost sensors, KF has become The most important estimation techniques in multi-sensor fusion integration, such as positioning, integrated navigation and network commutation. In acquiring process noise and measurement The classical or standard KF is optimal when accurate statistics of the amount of noise are obtained. However, since noise depends on the environment as well as the system Dynamic uncertainties and ot...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06F17/16G06F17/18G01C21/20
CPCG06F17/16G06F17/18G01C21/20G06F18/251Y02T90/00
Inventor 袁宏伟宋信敏刘正
Owner SHANDONG NORMAL UNIV
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