An Improved Kalman Filtering Method Based on Least Squares and Multiple Fading Factors

A technique of fading factor and least squares, which is applied in the field of improved kalman filtering based on least squares and multiple fading factors, can solve the problems of absolute optimality of unfavorable filters, large amount of calculation, etc., and suppress the problem of filter divergence , High real-time filtering and high filtering precision

Active Publication Date: 2021-05-04
HOHAI UNIV
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Problems solved by technology

However, in the current adaptive fading kalman filter algorithm, most of the fading factors are single fading factors, which is not conducive to the absolute optimality of the filter, and other methods for calculating multiple fading factors generally have a large amount of calculation. And in the calculation process, some matrices must be full rank

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  • An Improved Kalman Filtering Method Based on Least Squares and Multiple Fading Factors
  • An Improved Kalman Filtering Method Based on Least Squares and Multiple Fading Factors
  • An Improved Kalman Filtering Method Based on Least Squares and Multiple Fading Factors

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[0074] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0075] An improved kalman filtering method based on least squares and multiple fading factors, such as figure 1 shown, including the following steps:

[0076] Step 1. According to the sensor measurement information, obtain the time series p before filtering and the position measurement value y of the tracking target at the corresponding time p , where p=1,2,...,m, m is the initial moment of filtering, the least squares fitting is performed on the position measurement value, and the initial value of filtering is calculated;

[0077] In step 1, the initial value of the filter is calculated, as follows:

[0078] Step 1.1, using the least squares method to fit the time series collected in step 1 and the corresponding position measurement values ​​of t...

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Abstract

The invention discloses an improved kalman filtering method based on least squares and multiple fading factors. The invention uses the least squares method to select the initial value of the filter before the filter starts, reduces the deviation of the initial value, and uses new information in the process of filtering. The variance is calculated to obtain multiple fading factor matrices, and then the prediction error covariance is corrected, so as to realize the improvement of the adaptive fading kalman filter of single fading factor. The invention can effectively suppress filtering divergence, has high filtering precision, small amount of calculation, and high real-time performance.

Description

technical field [0001] The invention relates to the technical field of digital filtering and filtering divergence suppression, in particular to an improved kalman filtering method based on least squares and multiple fading factors. Background technique [0002] The Kalman filtering algorithm is a time-domain filtering method in the sense of minimum mean square error. When the mathematical model of the system and the statistical characteristics of process noise and measurement noise are known, the system state is obtained in real time in a recursive form. The best estimate of the variable. In practical engineering applications, the measured value of the sensor at the time of filtering is generally used as the initial value of the filter, but due to the influence of measurement noise, the sensor will have a certain random error when performing tracking measurement, resulting in the data measured by the sensor at the initial time of filtering may be There is a large deviation ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03H17/02
CPCH03H17/0202H03H2017/0205
Inventor 叶彦斐陈刚陈恒黄家辉童先洲
Owner HOHAI UNIV
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