1 An Algorithmic and Systematic Approach for Improving Robustness of TOA-based Localization Yongcai Wang, Lei Song Institute for Interdisciplinary Information.

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An Algorithmic and Systematic Approach for Improving Robustness of TOA-based

Localization

Yongcai Wang, Lei Song

Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, Beijing, China

in EUC2013, Nov.13, 2013

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Positioning by Time of Arrival (TOA) of ultrasound

Advantages: Accurate: Centi-meter level positioning accuracy Safe: user-imperceptible Low cost: US transducers are cheap (around 10 RMBs).

Context

ultrasoundRF

TOA1

0 t

ultrasoundRF

TOA3

0 t

ultrasoundRF

TOA2

0 t

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Challenges： Sensitive to Environment

Challenge 1: Non-Line-of-Sight Impacts1. Non-Line-of-Sight (NLOS) paths, caused by furniture,

doors, moving people may lead to large positioning error.

NLOS is generally inevitable

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Challenges (2): Miss of Synchronization

2. Background RF signal from WiFi, microwave oven, etc may collide the synchronization RF signal, leading to positioning error.

T1 T2

TOA1

TOA1

TOA2

TOA2

Interference from background RF is also inevitable

R1 R2

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Our Work

We show NLOS outlier detection problem is NP-hard.

We developed COFFEE, an iterative clustering, voting and filtering algorithm to detect NLOS distances.

First-Falling-Edge robust time synchronization

A prototype of Dragon system which implements COFFEE and First-Falling-Edge time synchronization.

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1. NLOS Outlier Detection Problem

N beacons with known coordinates

N beacons take N distance measurements:

m of the distances are NLOS outliers: m<N/2

NLOS detection Problem:

To detect the m outliers among the N distances.

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Conventional Approach: 1. Geometrical method

Outlier detection by Triangular Inequality [zhao2008].

Graph embeddability and rigidity. [Jian 2010] High computation cost. May fail to detect outlier when normal ranging distances

have noises.

Coarse-grained, may fail to detect the outlier

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Conventional Approaches 2: Least Trimmed Square Method [Pireto2009]

is a subset of distance measurements

Enumerate Ds to find the set with the minimum positioning residue.

N distances can generate at most O(2N)

subsets. Searching all sets needs high computation cost

Problem:

Method:

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Our Approach: Clustering and Filtering (COFFEE)

Distance measurements

Assign doubting weight

Potential positions

Density-based clustering

Core cluster

Position outlier

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2

1

Filter outlier distance

Delete outlier positions

COFFEE: Conduct clustering and filtering iteratively on the bipartite graph

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N=15m=3

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Algorithm Properties

Convergence speed Detect m distance outliers in m iterations.

Complexity N4logN

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COFFEE performs best in positioning accuracy improvement

Coffee provides the best accuracy

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COFFEE is Robust to the number of the distance outliers

Positioning error of COFFEE is small until m=8

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2. First-Falling-Edge Time Synchronization

A hardware type design Using a sync-line to connect all the receivers (beacons)

All receivers can be synchronized only if one receiver detects the synchronization RF signal.

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Robust Synchronization By First-Falling-Edge

When probability of missing RF signal is 1%

It helps more receivers to provide correct ranging, which improves the positioning accuracy.

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3. Prototype System (Dragon)

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Deployment of Dragon System

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Experiment Results

Without sync-line With sync-line

Positioning failure probability

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Indoor Positioning Error

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Reason of Positioning Error in Dragon

The ranging error caused by angle

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Conclusion

We proposed COFFEE, an efficient clustering and filtering algorithm for NLOS outlier detection. Accurate Low complexity Robust to the number of distance outliers.

First-Falling-Edge time synchronization improves the time synchronization probability effectively.

We developed a prototype of Dragon system, which verified the effectiveness of above designs.

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Thanks a lotFor your patience

Visit my homepage for further information

http://iiis.tsinghua.edu.cn/~yongcai