Indoor positioning method based on channel response frequency domain and airspace combined processing

Abstract

The invention discloses an indoor positioning method based on channel response frequency domain and airspace combined processing. The method comprises the following steps: discretizing a positioning area in an indoor environment and generating a plurality of reference points in an offline stage; enabling the mobile terminal to move at a constant speed at each reference point, acquiring channel information in two mutually perpendicular directions, preprocessing the acquired data, extracting corresponding fingerprint characteristics from the acquired data, and establishing an offline database byutilizing a relation between fingerprints in the mutually perpendicular directions; collecting the signal data of an unknown motion terminal in the online positioning stage, extracting the online fingerprint information, matching the fingerprint information of a to-be-tested terminal with fingerprints in an offline database, calculating the position of the to-be-tested terminal according to the similarity criterion, and outputting the position information of the to-be-tested terminal through the offline database and completing the indoor positioning. Rapid positioning of a target is realized,and the positioning time is effectively reduced. Position estimation is carried out by using a WKNN algorithm, so that more accurate positioning is realized.

Description

technical field [0001] The invention belongs to the technical field of indoor positioning, and in particular relates to an indoor positioning method based on joint processing of channel response frequency domain and air domain. Background technique [0002] There are many kinds of existing indoor positioning methods, such as infrared sensor (Infrared Sensor), ultrasonic (Ultrasonic Wave), ultra wide bandwidth (Ultra Wide Bandwidth), RFID (Radio Frequency Identification) and so on. Although these methods have high positioning accuracy, they all need to install special hardware equipment, and some hardware is expensive and not suitable for mass deployment, which limits their popularization. [0003] Traditional WIFI indoor positioning methods include: distance model-based and fingerprint information-based methods. The biggest problem with these two methods is the problem of positioning accuracy. The WIFI signal propagation environment for indoor positioning is not only compl...

AI Technical Summary

Problems solved by technology

The WIFI signal propagation environment for indoor positioning is not only complex, such as multipath effects, shadows, fading, and delay distortion, but also easily blocked by crowds or obstacles, resulting in large signal fluctuations

If you directly use the WIFI signal strength to do simple geometric algebra operations, it will often lead to large deviations in positioning results

The limited bandwidth of the WIFI system cannot accurately estimate the arrival time or angle of arrival of the signal, so there will be no more accurate positioning results

[0004] However, in the existing positioning methods based on WIFI fingerprint identification, most of the functional relationship between fingerprints and positions is not clear. When there is strong interference indoors, the robustness of fingerprint features is poor, which makes the positioning accuracy poor.

In addition, fingerprint information of a large number of reference points needs to be collected indoors in advance. When the indoor area is too large, the database is often relatively large

This makes the computational complexity and time complexity of online positioning high

Therefore, the current WIFI-based fingerprint recognition scheme can only be in the experimental stage.

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