Matrix filling based location fingerprint database construction method

Abstract

The invention provides a matrix filling based location fingerprint database construction method. According to the method, the fingerprint data on all reference points can be recovered from parts of known reference points, and various noises and other interference signals are take into consideration. The matrix model established in the invention can acquire various noise matrixes, and by deleting the noise points in the constructed fingerprint database, a good fingerprint data sample source can be provided for indoor positioning algorithm. Compared with existing fingerprint database construction methods based on propagation models and interpolations, the matrix filling based method can well recover the whole fingerprint database by means of a small number of acquisition samples, reduces the fingerprint data sampling workload while enhancing the positioning accuracy, and the model can still exhibit good robustness when it is affected by noises at varying degrees. While ensuring the positioning accuracy, the method also can save fingerprint data acquisition time, and also can eliminate the influence of noise and abnormities in the environment.

Description

technical field [0001] The invention relates to indoor positioning technology, in particular to a method for constructing a location fingerprint library based on matrix filling. Background technique [0002] With the rapid development of wireless communication technology, location service (LBS) realized by using various wireless local area networks and wide area networks has become a hot spot of development at present. More and more users have increasing demand for location information to meet their daily work and life. The key problem to be solved in the location service is the positioning problem. [0003] The current various positioning systems provide the necessary means for the development of location services and their various applications. In the indoor positioning system based on WLAN, there are mainly two positioning methods based on geometric measurement and scene analysis. Among them, the location fingerprint algorithm based on scene analysis is considered to be...

AI Technical Summary

Problems solved by technology

Of course, it takes a lot of time and energy to obtain the fingerprints of all reference points in the offline phase. In addition, as the surrounding environment changes and the number or layout of APs changes, the fingerprint database needs to be re-established. This greatly increases the cost of system operation and maintenance.

[0012] The second is the method based on the wireless propagation model. This method uses the signal propagation model to construct a fingerprint database in the entire positioning area through part of the collected fingerprint data. This method is relatively simple and easy to implement, but due to the diversity and complexity of the indoor environment However, the usual propagation model cannot accurately model problems such as multipath fading and interference, which affects the effectiveness of the built fingerprint database

[0013] The third is the interpolation method, which uses an interpolation function describing the relationship between the position of the reference point and the signal strength to construct the fingerprint of the entire positioning area. Compared with the wireless propagation model method, this method has better performance, but in order to obtain a better High positioning accuracy, the interpolation method still needs more sampling data to build an effective entire fingerprint database, and the workload of fingerprint data collection in the early stage is still relatively large

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