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Abstract The fingerprint indoor localization method based on channel state information (CSI) has gained widespread attention. However, this method fails to provide a better localization effect and higher localization accuracy due to poor fingerprint accuracy, unsatisfactory classification and matching effect, and vulnerability to environmental impacts. In order to solve the problem, this paper proposes a CSI fingerprint indoor localization method based on the Discrete Hopfield Neural Network (DHNN). The method mainly consists of off-line and on-line phases. At the off-line phase, a low-pass filter is applied to conduct a preliminary processing on the fingerprint information of each reference point, and then, phase difference is adopted to correct the fingerprint data of all reference points. In this way, the quality of fingerprint data is improved, hence avoiding problems such as indoor environmental changes and multipath effect of signals, etc. in which impact the fingerprint data. Finally, the characteristic fingerprint database is established after acquiring relatively accurate fingerprint data. At the on-line phase, to maintain the consistency of data, the data of each reference point in the fingerprint database is set as an attractor. Meanwhile, the localization information of the test point is processed to make convergence judgment through DHNN. Eventually, the localization result is obtained. The experimental results show that the localization accuracy with a median error of 1.6 m can be achieved through the proposed method in the experimental environment. Compared with similar methods, it has a higher stability which can significantly reduce the cost of manpower and time.

The device-free channel state information indoor fingerprint localization method may lead to phase offset errors, strong fingerprint noise and low sampling classification accuracy. In light of these characteristics, this article presents an indoor localization algorithm that is based on phase difference processing and principal component analysis. First, during the offline phase, this algorithm calculates phase differences to correct for random phase shifts and random time shifts in communication links. Second, the principal component analysis method is used to reduce the dimensionality of the denoised data and establish a robust fingerprint database. During the online phase, the algorithm trains a back-propagation neural network using the fingerprint data and determines the modelled mapping relationship between the fingerprint data and the physical localization after carrying out the phase difference correction and the principal component analysis–based dimensionality reduction. The experiments show that compared with existing fingerprint location methods, this algorithm has the advantages of significant denoising effectiveness and high localization accuracy.

Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing indoor positioning methods, however, are vulnerable to the environment and thus fail to fully reflect all the position features, due to limited accuracy of the fingerprint. As a solution, a CSI-based passive indoor positioning method was proposed, Wavelet Domain Denoising (WDD) was adopted to deal with the collected CSI amplitude, and the CSI phase information was unwound and transformed linearly in the offline phase. The post-processed amplitude and phase were taken as fingerprint data to build a fingerprint database, correlating with reference point position information. Results of experimental data analyzed under two different environments show that the present method boasts lower positioning error and higher stability than similar methods and can offer decimeter-level positioning accuracy.