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Indoor fusion positioning method based on extended Kalman filtering and particle filtering

An extended Kalman and particle filter technology, applied in the field of wireless sensor networks

Active Publication Date: 2019-12-20
JIANGNAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Aiming at the shortcomings of a single traditional indoor positioning technology, the present invention provides an indoor fusion positioning method based on extended Kalman filter and particle filter, by using extended Kalman filter to analyze the positioning results of WiFi fingerprint method and pedestrian dead reckoning algorithm Fusion, which effectively compensates for their respective deficiencies

Method used

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  • Indoor fusion positioning method based on extended Kalman filtering and particle filtering
  • Indoor fusion positioning method based on extended Kalman filtering and particle filtering
  • Indoor fusion positioning method based on extended Kalman filtering and particle filtering

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Effect test

Embodiment 1

[0116] The present invention collects and analyzes data in a laboratory with an area of ​​more than 100 square meters, and the area includes the coverage of WiFi wireless signals. The Huawei P8 (Android 4.0 platform) smartphone was used for data collection.

[0117] Using the signal strength information of WiFi to perform fingerprint positioning, the specific steps are as follows:

[0118] a) collect the wifi signal strength and the coordinates of the known location, and construct a fingerprint database;

[0119] b) use the k-nearest neighbor method to perform fingerprint positioning;

Embodiment 2

[0121] The user walks in a clockwise direction, and collects acceleration, magnetic field strength, and angular velocity with a smartphone.

[0122] By estimating the pedestrian's step length and motion direction, the next step position is calculated. The specific formula is as follows:

[0123]

[0124] The specific steps of pedestrian dead reckoning are as follows:

[0125] a) Using the acceleration differential finite state machine to realize step counting detection;

[0126] b) Estimate the step size with a method based on Kalman filtering;

[0127] c) Fusion direction sensor, gyroscope data to get heading. The formula used for weighted fusion is as follows:

[0128] Ori fuse,t = p · Ori o,t +(1-p) Ori g,t

[0129] In the experiment, the non-linear step size estimation models before and after improvement are used to estimate the step size respectively. Among them, the parameter K of the nonlinear model is taken as 0.35. Obtain the heading angle obtained by the ...

Embodiment 3

[0132] Use the extended Kalman filter algorithm to fuse the WiFi positioning results with the pedestrian dead reckoning positioning results. The specific steps are as follows:

[0133] a) Obtain the prior information of the state according to the system model:

[0134]

[0135] b) using the positioning result of the WiFi fingerprinting method as the observed value of the state vector;

[0136] c) Combine system model and measurement model for status update:

[0137] x k =X k|k-1 +K k (Z k -Z k|k-1 )

[0138] Among them, the initial value of the estimated error covariance of the extended Kalman filter is set to The covariance of the process noise is set to The covariance of the measurement noise is set to

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Abstract

The invention discloses an indoor fusion positioning method based on extended Kalman filtering and particle filtering, and belongs to the technical field of wireless sensor networks. The method comprises the following steps: (1) firstly, acquiring signal intensity of WiFi and recording position coordinates to construct a fingerprint database, and then positioning by adopting a weighted k-nearest neighbor method; (2) collecting MEMS inertial sensor data, counting steps in combination with acceleration and a step counting algorithm based on a differential acceleration finite-state machine, fusing multiple sensor readings for course estimation, and estimating step length in combination with Kalman filtering and a nonlinear step length model; (3) using extended Kalman filtering to fuse a positioning result of a WiFi fingerprint method and pedestrian dead reckoning; and (4) correcting the estimated position by combining particle filtering and indoor map information. Through fusion, the problem that the positioning precision of a WiFi fingerprint method is easily influenced by signal fluctuation and the problem that positioning errors of a pedestrian dead reckoning method are accumulatedalong with time increase are solved, and the positioning precision can be remarkably improved.

Description

technical field [0001] The invention relates to an indoor fusion positioning method based on extended Kalman filtering and particle filtering, and belongs to the technical field of wireless sensor networks. Background technique [0002] With the development of wireless communication technology and the popularization of mobile smart terminals, indoor positioning has become a popular application in the current information industry and presents a broad market prospect. WiFi fingerprinting and pedestrian dead reckoning are two of the most commonly used positioning methods that do not require the deployment of expensive hardware infrastructure. The pedestrian dead reckoning method has a high positioning accuracy in a short period of time, but the positioning error gradually increases over time; and When using WiFi positioning, the positioning accuracy is easily affected by signal fluctuations. It is characterized by large single-point positioning errors but no error accumulation....

Claims

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Application Information

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IPC IPC(8): H04W4/029H04W4/33H04W64/00G01S11/06G01S5/02
CPCG01S5/0257G01S11/06H04W4/029H04W4/33H04W64/00
Inventor 张雨婷陈璟
Owner JIANGNAN UNIV
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