Passive Indoor Localization

8/11/2019 Passive Indoor Localization

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Passive Indoor Localization

Sameera Palipana

NIMBUS Centre for Embedded Systems Research

Department of Electronic EngineeringCork Institute of Technology

Ireland

www.nimbus.cit.ie


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DFL Techniques

Direct coordinate estimation

Tomography

Radio Tomographic Imaging (RTI)

Radio Frequency Tomography (RFT)

Fingerprint matching

MIMO radar


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Direct Coordinate Estimation

Uses RSS information

Study the behaviour of RSS in the presence of

humans

Localize using heuristic methods

Midpoint algorithmsingle object, no calibration

Intersection algorithmsingle object, no calibration

Best cover algorithmhigher accuracy, calibration

Probabilistic cover algorithmhigh accuracy, multiple

objects


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[1]Dian Zhang; Jian Ma; Quanbin Chen; Ni, L.M.,

"An RF-Based S stem for Trackin Transceiver-Free Ob ects " PerCom '07. March 2007

Midpoint algorithmIntersection

algorithm

Best cover algorithm


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Environment: Uncluttered indoor

Tracks multiple objects

When objects are closer, identified as one object

Difficulty in tracking when paths intersect

Scaled well to fit a larger area

23 sensors in 64m Accuracy

Single object 0.8m, multiple objects: 1m

Low latency: 2s

Depends on channel characteristics and algorithms for better

results


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Tomography

Radio Tomographic Imaging Measures the attenuation (shadowing loss) of an obstacle

Estimates the image

Linearized shadowing model

For link i

+ + = = + For all links: + is the attenuation image Inverse is found using least squares + regularization

=shadowing loss of link iat time t() = attenuation of voxeljat time t = weighting of pixelj for link Id = distance between two nodes

= tuneable parameter 1 , 2 = distance from node 1and 2to voxelj

()

= Shadowing loss approximated as a sum ofattenuation of each voxel

if 1 + 2 < +

Transmitted power =Multipath fading= Static losses due to distance, antennapatterns

= measurement noise


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Radio Tomographic Imaging

Image accuracy depends on

Node density ->higher density gives better accuracy

Node formation-> nodes should surround the area

Regularization parameter Strong regularization->lesser obstruction boundary

Weak regularization->higher noise

Weighting parameters

[2] Wilson, J.; Patwari, N., "Radio

Tomographic Imaging with Wireless

Networks," Mobile Computing, May 2010


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Radio tomographic Imaging

Types

RSS

Shadowing based

Measures the attenuation using mean RSS

Variance based Variance of the RSS caused by moving objects

Better results for moving objects

Kernel distance based

Kernel distance of the RSS distribution

Quantifies the changes in mean, variance and few otherqualities of the random variable in one metric

TOF


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Disadvantages of RTI- Quantization errors: Information can be lost in the two step imaging

process

-An imaging problem must be solved to track the object

- Limited resolution: Unlike in x-ray, because the wave length is 12.5cm at

2.4 GHz

- Recalibration is needed over long periods

- Solution: Online Calibration

- E.g. Background subtraction, foreground detection

- Sensors cant be placed on different positions

- E.g. ceiling


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Radio Frequency Tomography

Get sensor measurements with static obstructions Calculate background RSS vector During tracking period

Collect RSS vector Calculate attenuation

Use a particle filter to track the marginal posterior distribution

p(|:) Estimate the expected value of

= object position= object attenuation= RSS at time k= RSS w/o obstruction


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Radio Frequency Tomography

Advantage over RTI

Eliminates the two step process in RTI

Reduces quantization error

Particle filters

Used for multiple target detection

Real-time object detection

Maximum targets: 4

Recalibration is needed

Sensors cant be placed on different positions

E.g. ceiling


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Advantages of tomography

Clearly defined models

Channel characteristics

Measurement

Different areas to focus Using channel diversity to increase accuracy

Fading characteristics (Anti-fade links give best results)

Sensor rotation

Particle filtering

Widely researched area


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MIMO radar

Multiple transmit and receive antennas increases the resolution of the radar

image

Resolution of an image aperture length length of the antenna array

Virtual array of antennas increase the aperture length

A sensor node can replace an antenna

This results in higher image resolution, improved parameter identification and better

accuracy in target localisation

Less research is published in this area

[3] Ali, T.; Sadeque, A.Z.; Saquib, M.;

Ali, M., "MIMO Radar for Target

Detection and Localization in Sensor

Networks," Systems Journal, IEEE,

March 2014


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Comparison

Work No. Environment Node

Density

Error Latenc

y

Features

Direct

coordinate

estimation

[4]

1

2

Uncluttered

indoor.15 .8m

1m

2s The objects are not

too tightly close to

each other

RTI

[5]

4 Uncluttered indoorApartment

Office

.43

.57

.48.55m Real

time

Machine vision

based,

Allows Intersections

RFT

[6]

1

2

3

Uncluttered

indoor.375

.3m

.7m

.8m

.2s

.7s

1.6s

Targets fixed

Fingerprint

[7]

4 OfficeUncluttered indoor

.15

.051.08m

1.49m

.88s Partially

overlapping

trajectories


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Open challenges

- Multiple object tracking (no. of objects> 4)

- Density estimation

- Through wall tracking (moving and stationary)

- Reduce energy consumption of the network

- Sensor deployment- Current methods require sensors to surround the tracking area in RTI

- Use of different measurements:

- e.g. time of flight in RTI for 8m8m area with only 8 nodes achieves

better accuracy than RSS based RTI [9]- Reduce node density

- density 0.48, 32 nodes per 67- Methods not requiring recalibration with good accuracy in RTI

[3] RF sensor networks for device-free localization,

https://sites.google.com/site/boccamaurizio/research


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References

[1] Dian Zhang; Jian Ma; Quanbin Chen; Ni, L.M., "An RF-Based System for Tracking Transceiver-

Free Objects," PerCom '07. March 2007

[2] Wilson, J.; Patwari, N., "Radio Tomographic Imaging with Wireless Networks," Mobile

Computing, May 2010

[3] Ali, T.; Sadeque, A.Z.; Saquib, M.; Ali, M., "MIMO Radar for Target Detection and Localization

in Sensor Networks," Systems Journal, IEEE, March 2014

[4] D. Zhang, K. Lu, R. Mao, Y. Feng, Y. Liu, M. Zhong and L. Ni, 'Fine-grained Localization for

Multiple Transceiver-free Objects by using RF-based Technologies', IEEE, 2013.

[5] M. Bocca, O. Kaltiokallio, N. Patwari and S. Venkatasubramanian, 'Multiple target tracking

with RF sensor networks', IEEE, 2013

[6] C. Xu, B. Firner, R. Moore, Y. Zhang, W. Trappe, R. Howard, F. Zhang and N. An, 'Scpl: Indoor

device-free multi-subject counting and localization using radio signal strength', pp. 79--90, 2013.

[7] C. Xu, B. Firner, R. Moore, Y. Zhang, W. Trappe, R. Howard, F. Zhang and N. An, 'Scpl: Indoor

device-free multi-subject counting and localization using radio signal strength', pp. 79--90, 2013

[8] RF sensor networks for device-free localization,

https://sites.google.com/site/boccamaurizio/research

[9] Wang, Jie; Gao, Qinghua; Wang, Hongyu; Yu, Yan; Jin, Minglu, "Time-of-Flight-Based Radio

Tomography for Device Free Localization," Wireless Communications, IEEE Transactions on,

vol.12, no.5, pp.2355,2365, May 2013
https://sites.google.com/site/boccamaurizio/researchhttps://sites.google.com/site/boccamaurizio/researchhttps://sites.google.com/site/boccamaurizio/research


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Thank you

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Passive Indoor Localization