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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.

Ru J, Wu C, Jia Z, Yang Y, Zhang Y, Hu N - Sensors (Basel) (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Information, Northeastern University, Shenyang 110819, China. rujingyu@hotmail.com.

ABSTRACT
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.

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Related in: MedlinePlus

The trajectory of the MT in simulation.
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sensors-15-14298-f023: The trajectory of the MT in simulation.

Mentions: A real experiment is done in the third floor of the Comprehensive Technical Building of Northeastern University to show the effectiveness of the algorithm in complex indoor environments. For comparison purposes, the trajectory of MT is as the same as before, so are the parameters. The AP1 and AP2 are put in the corridor of the building, and the AP3 is put into a room and each AP is bound on a 1.5 meters tall table tripod. Some important signs are put on the ground of the aisle based on the calculation and measurement as shown as blue circle in Figure 23. A person takes a mobile node walking along the mark points to receive the signal from the three APs. After rounding one lap, the received data are transferred into the computer server with 16 cores and 32GB RAM to estimate the trajectory of MT.


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.

Ru J, Wu C, Jia Z, Yang Y, Zhang Y, Hu N - Sensors (Basel) (2015)

The trajectory of the MT in simulation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507710&req=5

sensors-15-14298-f023: The trajectory of the MT in simulation.
Mentions: A real experiment is done in the third floor of the Comprehensive Technical Building of Northeastern University to show the effectiveness of the algorithm in complex indoor environments. For comparison purposes, the trajectory of MT is as the same as before, so are the parameters. The AP1 and AP2 are put in the corridor of the building, and the AP3 is put into a room and each AP is bound on a 1.5 meters tall table tripod. Some important signs are put on the ground of the aisle based on the calculation and measurement as shown as blue circle in Figure 23. A person takes a mobile node walking along the mark points to receive the signal from the three APs. After rounding one lap, the received data are transferred into the computer server with 16 cores and 32GB RAM to estimate the trajectory of MT.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Information, Northeastern University, Shenyang 110819, China. rujingyu@hotmail.com.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus