Accelerometer assisted robust wireless signal positioning based on a hidden Markov model

Liu, Jingbin; Chen, Ruizhi; Pei, Ling; Chen, Wei; Tenhunen, Tomi; Kroger, Tuomo; Chen, Yuwei

doi:10.1109/plans.2010.5507251

Cited by 46 publications

(59 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The smartphone is a preferable platform for sensing human motion states because there are a number of built-in motion sensors such as accelerometers, gyroscopes and magnetometers [17,21]. This study concentrates on the recognition of human motion states such as sitting, standing and walking, which represent certain types of human activities that may result in location changes.…”

Section: Background Of Smartphone Mobility Sensingmentioning

confidence: 99%

“…At each reference point, emission probabilities of RSSI observables are learnt and recorded in the knowledge database, as illustrated in Figure 4. An initial position is determined using the maximum likelihood fingerprinting method [21], and then the initial state probability distribution is established according to the fingerprinting result. Motion dynamics information, including the traveled distance and direction during a measurement duration, is used to improve the precision of state transition probability described as below.…”

Section: Radio Signals and Mems Sensors Integration For Smartphone Pomentioning

confidence: 99%

“…The more precise the state transition probabilities are, the more reliable the estimate that can be achieved. In the general pedestrian dead reckoning (PDR) technique, accelerometers are used for step detection, and step length estimation, gyroscope and magnetometer sensors are combined to resolve the movement heading [21,32]. The movement heading is fused with the step length to calculate the accumulated position change relative to a previous position [21,43,51].…”

Section: Radio Signals and Mems Sensors Integration For Smartphone Pomentioning

confidence: 99%

“…In the general pedestrian dead reckoning (PDR) technique, accelerometers are used for step detection, and step length estimation, gyroscope and magnetometer sensors are combined to resolve the movement heading [21,32]. The movement heading is fused with the step length to calculate the accumulated position change relative to a previous position [21,43,51]. The PDR technique has high precision within a short period, although suffering from accumulated absolute positioning errors over time [2,32,43,51].…”

Section: Radio Signals and Mems Sensors Integration For Smartphone Pomentioning

confidence: 99%

“…Many past studies have presented many methods and results of the estimation of pedestrian location and motion states, which were usually conducted separately in respective framework [19][20][21][22][23][24][25][26][27][28][29][30][31][32]. This paper contributes in two aspects.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution

et al. 2015

Self Cite

View full text Add to dashboard Cite

Abstract:The rapid advance in mobile communications has made information and services ubiquitously accessible. Location and context information have become essential for the effectiveness of services in the era of mobility. This paper proposes the concept of geo-context that is defined as an integral synthesis of geographical location, human motion state and mobility context. A geo-context computing solution consists of a positioning engine, a motion state recognition engine, and a context inference component. In the geo-context concept, the human motion states and mobility context are associated with the geographical location where they occur. A hybrid geo-context computing solution is implemented that runs on a smartphone, and it utilizes measurements of multiple sensors and signals of opportunity that are available within a smartphone. Pedestrian location and motion states are estimated jointly under the framework of hidden Markov models, and they are used in a reciprocal manner to improve their estimation performance of one another. It is demonstrated that pedestrian location estimation has better accuracy when its motion state is known, and in turn, the performance of motion state recognition can be improved with increasing reliability when the location is given. The geo-context inference is implemented simply with the expert system principle, and more sophisticated approaches will be developed. OPEN ACCESSMicromachines 2015, 6 700

show abstract

Section: Background Of Smartphone Mobility Sensingmentioning

confidence: 99%

Section: Radio Signals and Mems Sensors Integration For Smartphone Pomentioning

confidence: 99%

Section: Radio Signals and Mems Sensors Integration For Smartphone Pomentioning

confidence: 99%

Section: Radio Signals and Mems Sensors Integration For Smartphone Pomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

Building Range Technologies

2019

Indoor Positioning

View full text Add to dashboard Cite

Multienvironment performance comparison of robot‐assisted indoor location estimation system

Perente

Şerif

2020

Concurrency and Computation

View full text Add to dashboard Cite

Many indoor location systems utilizing the fingerprinting technique require frequent signal map generation due to the dynamic properties of indoor environments. This time-consuming task can be freed from human involvement by using various techniques. Thus, a robot-aided signal map creator is implemented and its positioning performance as well as its mapping time is compared with a widely used human facilitated approach. For testing purposes, a low-cost, low-power, not off-the-shelf signal data collection robot is implemented. Its signal collection speed and location estimation precision is then compared with the manual method both in a small and large environment.The findings indicate that in the small environment, robot-aided approach performs with 268-cm error-rate 70% of the time. On the other hand, the improved robot-aided approach in large environment saves 52 min of labor-intense legwork and achieves 303.5-cm mean error-rate.

show abstract

Accelerometer assisted robust wireless signal positioning based on a hidden Markov model

Cited by 46 publications

References 26 publications

Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution

Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution

Building Range Technologies

Multienvironment performance comparison of robot‐assisted indoor location estimation system

Contact Info

Product

Resources

About