A novel indoor RSS-based position location algorithm using factor graphs

Huang, Chin-Tseng; Wu, Chunyong; Lee, Yao-Nan; Chen, Jiunn-Tsair

doi:10.1109/twc.2009.080452

Cited by 57 publications

(23 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation (8) can be rearranged as (9). Substituting all the points on the desired plane into (9) generates the equation of the plane constituted by points I, J, and K.…”

Section: Methodsmentioning

confidence: 99%

“…With the rapid development and proliferation of wireless local area networks [4], countless Wi-Fi access points (APs) have been set up in numerous places, such as airports, department stores, and schools. In addition to providing wireless Internet access, APs widely distributed in environments have been used for indoor positioning [5][6][7][8][9]. In 2000, Bahl et al [2] were the first scholars to propose an indoor positioning system, RADAR, which is a positioning method that collects signal strength characteristics in an environment to form a database and identifies locations based on these signal characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Because each environment has a plane model corresponding to each AP, detecting the environment at which a user is located reveals a plane model that can be used to position user locations. Moreover, a method involving equation solving can be adopted to replace the comparison of immense data for reducing computing complexity [9]. Databases can be combined with path-loss prediction model to minimize the time required for signal comparison [15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Actualizing real‐time indoor positioning systems using plane models

Yeh

2015

Int J Communication

View full text Add to dashboard Cite

SUMMARY Compared with other indoor positioning methods, pattern matching offers greater accuracy. However, its construction requires additional time and manpower cost, and larger positioning environments result in correspondingly greater construction costs and computing complexity. To reduce the cost and computing complexity of pattern construction, plane models featuring radio signals were adopted in conjunction with quick response codes to facilitate the simple and rapid construction of indoor environments in this study. The advantages of using plane equations instead of pattern matching radio maps for positioning computations include the following: (1) reducing the complexity of positioning computations; (2) eliminating the influences of positioning area size; and (3) providing reliable and accurate positioning with an average error of approximately 2.8 m. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

“…Equation (8) can be rearranged as (9). Substituting all the points on the desired plane into (9) generates the equation of the plane constituted by points I, J, and K.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Actualizing real‐time indoor positioning systems using plane models

Yeh

2015

Int J Communication

View full text Add to dashboard Cite

show abstract

“…To better reflect the local linearity feature in the proposed algorithm, the measured RSS truep^w,i is expressed in the form of logarithmic scale false(p^i=10·log10(truep˜w,i+ei)false). In addition, the logarithmic RSS has also been demonstrated to have an approximate Gaussian distribution [21]. The factor node Pi expresses the Gaussian statistical distribution relationship of the mean false(p˜ifalse) and variance false(σpi2false) generated by logarithmic RSS measurement.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…However, both TOA-FG and AOA-FG techniques are not suitable for indoor positioning due to the lack of line-of-sight (LOS) scenario. The RSS-FG technique not only overcomes the requirement of perfect synchronization or time stamp but also adapts the LOS and non-line-of-sight (NLOS) positioning scenario [21]. Yet, the RSS-FG method has been proved to be unable to achieve the localization of the unknown radio transmitter since both transmitting frequency and power of the radio transmitter are unknown.…”

Section: Introductionmentioning

confidence: 99%

Indoor 3-D Localization Based on Received Signal Strength Difference and Factor Graph for Unknown Radio Transmitter

Zhang

Jiang

2019

Sensors

View full text Add to dashboard Cite

Accurate localization of the radio transmitter is an important work in radio management. Previous research is more focused on two-dimensional (2-D) scenarios, but the localization of an unknown radio transmitter under three-dimensional (3-D) scenarios has more practical significance. In this paper, we propose a novel 3-D localization algorithm with received signal strength difference (RSSD) information and factor graph (FG), which is suitable for both line-of-sight (LOS) and non-line-of-sight (NLOS) condition. Considering the stochastic properties of measurement errors caused by the indoor environment, RSSD measurements are processed with mean and variance in the form of Gaussian distribution in the FG framework. A new 3-D RSSD-based FG model is constructed with the relationship between RSSD and location coordinates by local linearization technique. The soft-information computation and iterative process of the proposed model are derived by using the sum-product algorithm. In addition, the impacts of different grid distances and number of signal receivers on positioning accuracy are explored. Finally, the performance of our proposed approach is experimentally evaluated in a real scenario. The results show that the positioning performance of the proposed algorithm is not only superior to the k-nearest neighbors (kNN) algorithm and least square (LS) algorithm, but also it can achieve a mean localization error as low as 1.15 m. Our proposed scheme provides a good solution for the accurate detection of an unknown radio transmitter under indoor 3-D space and has a good application prospect.

show abstract

ZigBee-based intelligent indoor positioning system soft computing

Luoh

2013

Soft Comput

View full text Add to dashboard Cite

The positioning system consists of three parts, namely the hardware platform, the node communications and PC monitoring software program. Hardware platform mainly CC2530 chip as the core, including RF modules, auxiliary circuits, LED status indication circuit and so on. Node communication program is mainly based on the selection procedure for the preparation of localization algorithm, the process of selecting a location positioning algorithm based on acceptable signal strength indicator RSSI (Received Signal Strength Indication) in. PC monitoring software is written in Visual Basi6.0 environment, the main visualization graphics. According to the actual test, the more open the roof, the system can achieve accuracy error of less than 1m; in the classroom, and the system can achieve accuracy error of less than 2m. The wireless location system with low cost, low power consumption, high reliability notable features.

show abstract

A novel indoor RSS-based position location algorithm using factor graphs

Cited by 57 publications

References 12 publications

Actualizing real‐time indoor positioning systems using plane models

Actualizing real‐time indoor positioning systems using plane models

Indoor 3-D Localization Based on Received Signal Strength Difference and Factor Graph for Unknown Radio Transmitter

ZigBee-based intelligent indoor positioning system soft computing

Contact Info

Product

Resources

About