An Indoor Mobile Location Estimator in Mixed Line of Sight/Non-Line of Sight Environments Using Replacement Modified Hidden Markov Models and an Interacting Multiple Model.
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Consequently, to improve precision, each moving node uses the IMM model to integrate the results from the HMM and its modified forms.Simulation experiments conducted show that our proposed algorithms perform well in both distance estimation and coordinate calculation, with increasing accuracy of localization of the proposed algorithms in the order M-HMM, RM-HMM, and HMM + IMM.The simulations also show that the three algorithms are accurate, stable, and robust.
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Affiliation: School of Information, Northeastern University, Shenyang 110819, China. rujingyu@hotmail.com.
ABSTRACT
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Localization as a technique to solve the complex and challenging problems besetting line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions has recently attracted considerable attention in the wireless sensor network field. This paper proposes a strategy for eliminating NLOS localization errors during calculation of the location of mobile terminals (MTs) in unfamiliar indoor environments. In order to improve the hidden Markov model (HMM), we propose two modified algorithms, namely, modified HMM (M-HMM) and replacement modified HMM (RM-HMM). Further, a hybrid localization algorithm that combines HMM with an interacting multiple model (IMM) is proposed to represent the velocity of mobile nodes. This velocity model is divided into a high-speed and a low-speed model, which means the nodes move at different speeds following the same mobility pattern. Each moving node continually switches its state based on its probability. Consequently, to improve precision, each moving node uses the IMM model to integrate the results from the HMM and its modified forms. Simulation experiments conducted show that our proposed algorithms perform well in both distance estimation and coordinate calculation, with increasing accuracy of localization of the proposed algorithms in the order M-HMM, RM-HMM, and HMM + IMM. The simulations also show that the three algorithms are accurate, stable, and robust. Related in: MedlinePlus |
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Mentions: An instance of the value of the measured RSS power is shown in Figure 2. In the Figure 2, . Figure 2a shows the signal received by the AP at P disperse times. And in Figure 2b the solid blue line represents the absolute value of the received signal, and the red dotted line represents the fitting curve of the covariance at each time point. Figure 2c shows when an AP receives a set of signals, it can estimate the probability of the position of the source by rotating the power delay profile model. In the simulation for HMM, the NLOS delay has an exponential probability density function (PDF) with σδ = 10. In this paper, we assume that the NLOS delay is generated by σδ = 7. According to the nonparametric kernel method proposed by McGuire et al. [27], the estimated PDF of NLOS delay is as follows:(5)f(b)=12πPhij∑t=1Pexp(−(b−Sbijt)22hij2) |
View Article: PubMed Central - PubMed
Affiliation: School of Information, Northeastern University, Shenyang 110819, China. rujingyu@hotmail.com.