Basmag: An Optimized HMM-Based Localization System Using Backward Sequences Matching Algorithm Exploiting Geomagnetic Information

Ma, Yongtao; Dou, Zhi; Jiang, Qideng; Hou, Zhengxin

doi:10.1109/jsen.2016.2600099

Cited by 38 publications

(18 citation statements)

References 30 publications

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“…In order to better understand the geomagnetic multi-features positioning algorithm, Algorithm 1 gives the detailed process of it. The particle filter (PF) is an algorithm based on the Monte Carlo method [44] and the Bayesian approach [45], which has good performance in solving non-linear problems. Generally, a dynamic system can be assumed that it obeys the Markov process of order one, it can be modeled as follows:…”

Section: Appl Sci 2020 10 X For Peer Review 8 Of 22mentioning

confidence: 99%

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

Sun

Wang

et al. 2020

Applied Sciences

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This paper proposes a fusion indoor positioning method that integrates the pedestrian dead-reckoning (PDR) and geomagnetic positioning by using the genetic-particle filter (GPF) algorithm. In the PDR module, the Mahony complementary filter (MCF) algorithm is adopted to estimate the heading angles. To improve geomagnetic positioning accuracy and geomagnetic fingerprint specificity, the geomagnetic multi-features positioning algorithm is devised and five geomagnetic features are extracted as the single-point fingerprint by transforming the magnetic field data into the geographic coordinate system (GCS). Then, an optimization mechanism is designed by using gene mutation and the method of reconstructing a particle set to ameliorate the particle degradation problem in the GPF algorithm, which is used for fusion positioning. Several experiments are conducted to evaluate the performance of the proposed methods. The experiment results show that the average positioning error of the proposed method is 1.72 m and the root mean square error (RMSE) is 1.89 m. The positioning precision and stability are improved compared with the PDR method, geomagnetic positioning, and the fusion-positioning method based on the classic particle filter (PF).

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Section: Appl Sci 2020 10 X For Peer Review 8 Of 22mentioning

confidence: 99%

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

Sun

Wang

et al. 2020

Applied Sciences

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“…The HMMs have been used in some approaches for the integration of fingerprinting positioning and inertial navigation systems [10–12, 16–17]. In [10], Ni et al present a pedestrian positioning method using a HMM with a fuzzy pattern recognition algorithm in a WLAN fingerprint system.…”

Section: Related Workmentioning

confidence: 99%

“…Hidden Markov models (HMMs) [9] are powerful probabilistic tools for modelling sequential data, and have been applied with success to natural language processing, speech recognition and so forth. In recent years, HMMs are applied to indoor positioning tentatively for combining RSSI fingerprinting method with inertial sensors, and make some contributions [10–12]. In that case, positioning is considered as from an isolated location estimating process to a sequential locations transition process, and the observations (RSSI) and the states (locations) transition are usually modelled as Gaussian distributions over a discrete location.…”

Section: Introductionmentioning

confidence: 99%

Local HMM for indoor positioning based on fingerprinting and displacement ranging

Shao

et al. 2018

IET Communications

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“…However, highprecision indoor positioning based on GNSS has not been achieved since indoor GNSS signals are usually weak or even nonexistent. Therefore, some positioning technologies such as ultrawideband (UWB) [1,2], Bluetooth [3], WiFi [4,5], radio frequency identification (RFID) [6,7], computer vision [7], ultrasonic [8], inertial navigation system (INS) [9], pseudolite [10], and geomagnetic fields [11] were presented to achieve indoor positioning with high accuracy and strong availability.…”

Section: Introductionmentioning

confidence: 99%

WiFi RTT Indoor Positioning Method Based on Gaussian Process Regression for Harsh Environments

Cao

Wang

et al. 2020

IEEE Access

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A novel two-way ranging approach was introduced into the Wireless Fidelity (WiFi) standard, and its ranging accuracy reached one meter in a low multipath environment. However, in harsh environments due to multipath or non-line of sight (NLOS), the range measurement based on the WiFi round trip time (RTT) usually has low accuracy and cannot maintain the one-meter accuracy. Thus, this paper proposes an indoor positioning method based on Gaussian process regression (GPR) for harsh environments. There are two stages in the proposed method: construction of a positioning model and location estimation. In the model construction stage, based on known positions of access points (APs), we can determine the position coordinates of some ground points and the reference distances between them and the APs, and the offline ranging difference fingerprints can be generated by the reference distances, which means that there is no need to collect data. Gaussian process regression (GPR) utilizes offline ranging difference fingerprints based on the reference distance to establish a positioning model, and the particle swarm optimization (PSO) algorithm is employed to estimate the GPR hyperparameters. In the location estimation stage, the gathered actual range measurements generate the online ranging difference fingerprint, which is the input data of the positioning model. The output of the model is the estimated position of the smartphone. Experimental results show that the mean errors (MEs) of the proposed method and Least Squares (LS) algorithm are 1.097 and 3.484 meters, respectively, in a harsh environment, and the positioning accuracy of the proposed method improved by 68.5% compared with the LS algorithm.

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Basmag: An Optimized HMM-Based Localization System Using Backward Sequences Matching Algorithm Exploiting Geomagnetic Information

Cited by 38 publications

References 30 publications

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

Local HMM for indoor positioning based on fingerprinting and displacement ranging

WiFi RTT Indoor Positioning Method Based on Gaussian Process Regression for Harsh Environments

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