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CSI system multipath classification method based on improved random forest algorithm

A technology of random forest algorithm and classification method, which is applied in the field of multipath classification of CSI system, and can solve the problems that the classification accuracy rate needs to be further improved

Pending Publication Date: 2020-05-29
TIANJIN POLYTECHNIC UNIV
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AI Technical Summary

Problems solved by technology

In order to improve the accuracy of indoor positioning, some scholars have done related research on multipath classification, but the classification accuracy needs to be further improved.

Method used

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  • CSI system multipath classification method based on improved random forest algorithm
  • CSI system multipath classification method based on improved random forest algorithm
  • CSI system multipath classification method based on improved random forest algorithm

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Embodiment Construction

[0020] First, build a data acquisition system based on computers and routers equipped with wireless network cards, collect CSI data in LOS and NLOS environments, and extract features from preprocessed samples to improve classification performance, and introduce K-means aggregation The Class Algorithm proposes an eigenfactor based on the K-means clustering algorithm.

[0021] Such as figure 2 Shown is the flow chart of eigenfactor construction based on K-means clustering algorithm. In view of the CIR samples in the LOS environment, the signal energy is mainly concentrated in the main path, and the sampling points adjacent to the main path are samples belonging to the same category as the main path, and the energy is relatively high. Other signals that propagate through the reflection path The energy is relatively small. Under NLOS propagation conditions, there is no direct line-of-sight path in the signal propagation process, so the signal energy distribution of each subcarr...

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Abstract

The invention belongs to the field of wireless positioning, and relates to a CSI system multipath classification method based on an improved random forest algorithm. The method aims at distinguishingtwo propagation modes of LOS and NLOS, and comprises the following steps: clustering the energy of all samples by using a K-means clustering algorithm according to the propagation characteristics of wireless signals in LOS and NLOS environments, and constructing a characteristic factor based on the K-means clustering algorithm; calculating inter-class scattering distances and intra-class scattering distances of all the samples, and obtaining an optimal feature combination according to a Fisher criterion; and training and testing different feature combinations by using a random forest algorithmbased on a C4.5 algorithm to complete multipath classification. The method has the advantages that the phenomenon that multipath classification is limited by an accurate threshold value is effectively avoided, and the requirements for high accuracy and low operand in a multipath classification application scene can be met.

Description

technical field [0001] The invention belongs to the field of wireless positioning, and relates to a CSI system multipath classification method based on an improved random forest algorithm. Background technique [0002] WiFi positioning technology has the advantages of economy, convenience, quickness and easy deployment. In recent years, it has been successfully applied in many fields such as industrial automation, commercial automation, and transportation control management. Especially in indoor positioning application scenarios, ideally, the WiFi positioning system can obtain high positioning accuracy, showing great development potential and practical value. [0003] In WiFi-based indoor positioning systems, positioning accuracy is the fundamental index for evaluating positioning performance. The complexity and variability of indoor environments make positioning accuracy often subject to factors such as multiple access interference, time delay in circuits, and multipath pro...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62H04W4/021H04W4/33
CPCH04W4/021H04W4/33G06F18/23213G06F18/24323
Inventor 史伟光李耀辉李婉琪
Owner TIANJIN POLYTECHNIC UNIV
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