Adaptive Radio Interference Positioning System

Hao-ji Wu, Henry Chang, Bing You, Hao Chu, Polly

Huang National Taiwan University

Modeling and Optimizing Positional Accuracy based on Hyperbolic Geometry

The Short Story

• Improve accuracy of the RIP system (Vanderbilt U.)– RIP: Radio interference positioning

• Improvement comes from our adaptive mechanism– Model the error in the original RIP system

– From the error model, identify the controllable parameter (i.e., beacon node selection)

– Adapt based on the controllable parameter

• Implemented & evaluated in a real testbed– Verified our error model is accurate

– Showed our accuracy improvement is real.

Now the long story

4

Motivation

• Accuracy and precision are important performance goals in localization systems.– WiFi localization systems > 1 meter

– Look for a localization system with sub-meter positional accuracy (ideally with long range and low cost) similar to WiFi.

– Duplicate it and improve upon it.

(about the same time last year.)

5

Radio Interference Positioning (RIP) System• Proposed by Vanderbilt University (Kusy et al)

– Found it in Sensys 2005– Recent papers: Sensys 2007, Mobisys 2007, IPSN 2006, etc.

• High accuracy– Average accuracy 3 cm (Sensys’05)

• Long sensing range– 160 meters (Sensys’05), few nodes can cover wide area

• Low cost– Using standard sensor network nodes (Mica2), no additional

specialized hardware

• Amazing accuracy?– Mica2 node dimension (5.8 x 3.2 x 0.7) cm

6

Two starting questions

• Are the RIP system results reproducible?

• Can the RIP system be improved further?

7

Our Findings

• Are the RIP results reproducible?– Reproduced results

• Average: 75 cm; 90%: 1.41 meters

– Experimental setup is slightly different form Vanderbilt U.• 900 Mhz versus 433 Mhz (Vanderbilt U.)• 3 cm is very ideal situation

• Can the RIP system be improved further?– More room for improvement in our adaptation mechanism

8

Overview of RIP system

• Two phases• In the ranging phase: two ranging rounds• In each ranging round, we need to select

two beacon nodes (called sender-pair)– Target is a receiver

– Another infrastructure node is a receiver

• Why two beacon nodes?

Ranging Positioning

Anchor node(known location)

• A, B are senders– Transmit at two nearby

frequency (900 MHz)

– Produce an interference wave with low-beat frequency envelop (350 Hz)

• C (target), D are receivers– Each detects phase, and

jointly the phase difference

– Map the phase difference to a distance difference | AC – BC |

From Sensys 2005 (Kusy et al)

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RIP ranging & positioning

• Ranging_result

= distance(T,sender1) – distance(T,sender2)

= d1 – d2

• Each ranging result in a

hyperbolic curve• Two ranging needed for positioning• Two sender-pairs selected in two

ranging round are called

Sender Pair Combination (SPC) T

sender1

sender2

sender1 sender2

d1

d2

d1

d2

11

Modeling Position Error in RIP

• Ranging phase– Imperfect measurements lead to ranging error, i.e., error in (d1 –

d2) ~ 26 cm, more or less independent of distance to the beacon

– Ranging error leads to shift in the hyperbolic curve

• Positioning phase– Compute intersection of two shifted hyperbolic curves (due to

ranging errors)

– Ranging error may amplify the positioning error to different amounts

• Depend on the geometry layout between target and SPCs• [an example]

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How geometry layout affects positional error?• Two geometry layouts from two different SPC selections• With the same amount of range error, Layout (A) has a

larger error than Layout (B), why?– Can you identify two geometric properties leading to the larger

error in (A)?

sender1

sender2

sender1 sender2

sender1

sender2sender1

sender2

Errorrange

T

T’

T

T’

(A) (B)

13

Geometric property #1

• The position of T on the curve– Closer to the foci, smaller the error displacement from the curve,

and smaller the error amplification

sender1

sender2 sender1

sender2

Errorrange

T T

14

Geometric property #2

• Intersectional angle of hyperbolic curves• Shaper the intersectional angle, larger the error amplification

T’

T T

T’

T

T’

θ θ θ

θ= 90° θ= 60° θ= 30°

15

Geometric property #2 (cont.)

• Intersectional angle of hyperbolic curves• (A) has sharper intersectional angle than (B)

sender1

sender2

sender1 sender2

sender1

sender2sender1

sender2

Errorrange

T

T’

T

T’

θ θ

(A) (B)

16

Map Geometry properties to estimation error model

2

yx2range

error2

2

yxrange2

error2yxrange2

error2

yxrange1

error1

)T,(Tq

q tan

)T,(Tq

q

sin

sec)T,(Tq

q

)T,(Tq

q

PT

2

yx2range

error2

2

yxrange2

error2yxrange2

error2

yxrange1

error1

)T,(Tq

q tan

)T,(Tq

q

sin

sec)T,(Tq

q

)T,(Tq

q

PT

17

Validation of Estimation Error Model

Static RIP (6 anchor nodes, 10 meters radius)

Blue line : target pathBlue point : ground truthRed point : estimation position

18

Validation of Estimation Error Model

• Estimation error falls within 6 cm 90% of the time.

19

Adaptive RIP system

• We have an accurate Estimation Error Model– Predict Errorpositional using specific SPC

• We run Estimation Error Model for all SPC (exhaustive search), and find the SPC with minimum error

20

Evaluation of adaptive RIP system

• Single-target positioning experiment• Multi-target positioning experiment

A~F are anchor nodesEach grid is 1m2

21

Single-target positioning experiment

Static RIP Adaptive RIPBlue line : target pathBlue point : ground truthRed point : estimation position

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Average error (meter)

90-th percentile (meter)

Static RIP 0.93 1.66

Adaptive RIP 0.49 0.75

Improvement 47% 55%

Single-target positioning experiment

walking repeatedly 5 times, around 50 samples

23

Multi-target positioning experiment

• 6 targets– 1 moving (blue point)

– 5 static (1~5 green points)

24

Average error (meter)

90-th percentile (meter)

Static RIP 0.75 1.41

Adaptive RIP 0.30 0.54

Improvement 60% 61%

Multi-target positioning experimentStatic

25

Conclusion

• Error in RIP system is determined by geometric location of the target and beacons.

• We created an Error Estimation Model– Estimate error given relative location of the target and SPCs.

• Adaptive mechanism is built upon the error estimation model.– Find SPC with minimum error

• Showed that Error Estimation Model is accurate.• Showed that Adaptive mechanism improves accuracy.

26

Q & A

27

Flow of RIP system

1. Start RIP system

2. Select 2 anchor nodes as senders

3. Other anchor nodes become receiver

4. Ranging

5. If 1st ranging => goto 2 and do 2nd ranging

else => goto 6 do positioning

6. Positioning

7. End

T

Sender1

Sender2 Receiver2

Receiver1Sender1

Sender2Receiver2

Receiver1Ranging Positioning

Anchor node(known location)

1st ranging2nd ranging

28

Geometrical Derivation of Estimation Error Model• Steps:

1.Find TN (TM)

2.Find θ

3.Find PT geometrically

If we know TN, TM, θ, we can derivative PT geometrically

Known Variables:• Target location• SPC• Errorrange

MN

MN1-

mm1

m mtan

mN

mM

29

Original RIP system• Anchor nodes are placed in known locations• The positional error of RIP system is highly affected by target

locations and the selection of beacon nodes• Original RIP system is “static”

– The selection of beacon nodes is fixed, doesn’t change depend on target locations

T

Beacon

Beacon

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Our Contribution• Design, implement and evaluate an adaptive RIP system

– Dynamically select beacon nodes based on target locations

31

How SPC selection affect positional error (cont.)• Displacement of a hyperbolic curve

– Shortest distance from hyperbolic curve with error to target

sender1

sender2 sender1

sender2

Errorrange

T T

• PT is the estimation Errorpositional

• N is the projection point of T on H12

– TN is the displacement of hyperbolic curves H12

• θ is the intersectional angle of hyperbolic curves H12 and H34

• Find TM (TN) and θ geometrically

– Find PT geometrically

32

Map Geometry properties to estimation error model

Errorrange