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During the smartphone tracking process, after [xe.sub.pdr], the positioning results of PDR, and [xe.sub.rss], the positioning results of BRPV, are derived, the next key issue is how to fuse [xe.sub.pdr] and [xe.sub.rss].
Moreover, the initial position localized by BRPV only affects the accuracy at the beginning.
After BtPDR fuses the localization results of PDR and BRPV, it needs to adaptively update the step size of PDR.
Fortunately, experiment results show that the proposed BRPV and BtPDR can tolerate the suggested model errors, yielding satisfactory localization results.
To prove the localization accuracy of BRPV, simulations were carried out using MATLAB software.
This section analyzes the impact of the number of areas of BRPV. The plane is divided into three, five, seven, and nine areas, and the number of votes is assigned according to the probability that the smartphone falls into these areas.
In this section, BRPV is compared with the traditional least squares based multilateral positioning (LSMP) and fingerprint positioning (FP) methods.
10 it can be determined that 1) BRPV and FP have similar tolerance to noise, which is superior to the LSMP and 2) the localization accuracy of BRPV is slightly better than that of the FP and much better than that of the LSMP.
The comparison algorithms include the basic PDR in , the proposed BRPV, the LSMP in , and the Kalman filter-based fingerprint positioning (KFFP) algorithm in .
Therefore, to ensure that the fusion results of BRPV and PDR are reliable, [beta] = 18 was set in the simulation.
Compared with PDR, BRPV, LSMP and KFFP, BtPDR has improved positioning accuracy by 64.3%, 45.1%, 77.2% and 39.6%, respectively, in the case of a noise variance of 20, and has improved positioning accuracy by 49.4%, 48.3%, 80.6% and 34.9%, respectively, in the case of a noise variance of 50.
The BRPV only used the 4 largest Bluetooth beacon RSS to locate the smartphone, hence, the range of the number of votes for each voting area in BRPV was [4, 24].
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- Broz Josip