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Adaptive filtering method based on observation noise covariance matrix estimation

A technology for observing noise variance and adaptive filtering, which is applied in measurement devices, radio wave measurement systems, satellite radio beacon positioning systems, etc. The effect of small amount of calculation and storage, and practical value of extensive engineering

Inactive Publication Date: 2013-09-18
BEIHANG UNIV
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Problems solved by technology

However, if the one-step state predictor estimates Inaccurate, the estimation of the observation noise characteristics will be biased, which will affect the filtering accuracy

Method used

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  • Adaptive filtering method based on observation noise covariance matrix estimation
  • Adaptive filtering method based on observation noise covariance matrix estimation
  • Adaptive filtering method based on observation noise covariance matrix estimation

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Embodiment

[0118] The present invention is applied in the locomotive GPS / DR positioning system of Handan Steel Plant, as image 3 as shown, image 3 The locomotive in a is running in a relatively open area, image 3 b. image 3 c. image 3 The GPS on the locomotive in d is interfered by surrounding buildings, belt conveyors, and pipelines. During the locomotive running, the GPS measurement system has a large positioning error due to the obstruction of tall buildings, belt conveyors, and pipelines. If the locomotive passes the factory building Under the tracks, the GPS signal will be completely interrupted. In the locomotive work site, the road conditions are relatively complicated, the distance between adjacent railroad tracks is about 5m, and there are many obstacles. Due to the operation requirements, the locomotive runs at a low speed, which eventually leads to high dynamic GPS measurement noise during the locomotive positioning process. among the changes.

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Abstract

The invention discloses an adaptive filtering method based on observation noise covariance matrix estimation. The adaptive filtering method comprises the following steps of: constructing a mutual difference sequence and a radial-normal distance difference sequence by utilizing measurement complementary characteristics of different measurement systems in a combined navigation system so as to carry out dynamic estimation on a measurement noise characteristic of a single epoch, measuring an error level of noise mapping according to an estimation result, and constructing an adaptive factor by taking a preset filtering precision as an index to effectively regulate a filtering gain array so as to carry out adaptive Kalman filtering resolution. According to the adaptive filtering method based on the observation noise covariance matrix estimation, disclosed by the invention, high dynamic estimation of measurement noise characteristics is not only realized, but also the filtering gain array is effectively updated, and finally the location precision of the combined navigation system is improved.

Description

technical field [0001] The invention relates to an adaptive filtering method suitable for information fusion of an integrated navigation system, which can effectively estimate the dynamic characteristics of measurement noise in real time, avoid filtering divergence, and is used to improve the positioning accuracy of the integrated navigation system, belonging to the integrated navigation adaptive filtering technology field. Background technique [0002] The integrated navigation information fusion method usually adopts the Kalman filter method, and the standard Kalman filter is based on the optimal estimation of the known accurate model and noise characteristics. However, the acquisition of noise characteristics is generally based on prior statistical information, and any statistical information will inevitably be distorted, especially it is difficult to accurately describe the dynamic characteristics of the currently observed noise. In the study of dynamic estimation of ob...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S19/49
Inventor 张海苌永娜李玉洁周启帆王嫣然
Owner BEIHANG UNIV
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