Localization

Mahesh K. Banavar and Kevin Mack

CoSiNe Lab

Dept of ECE

Clarkson University

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Questions to Ponder

• Who am I?

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• What am I?

• Where am I?

Problems at Hand

• FCC report1: 90% of E911 calls have localization error greater than 100 meters – Over 56% of 911 calls made from indoor locations

– Wrong location could put the caller in different rooms or even different floors

• Consider other problems – Emergency responders in a disaster area – no

infrastructure

– Navigation in malls/hospitals/schools

1Working Group 3, “E9-1-1 Location Accuracy: Indoor localization test bed report,” The Communications Security, Reliability and Interoperability Council, March, 2013.

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How is localization done?

Three modes used in general: • Range based

– Compute the distance between the target and reference points

• Angle based – Find the direction in which the target is with respect

to the reference point – Modes: DOA (AOA) – Applications: Triangulation, array-based systems,

humans!

• Hybrid

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Range-based Localization

• We want to find the target:

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Use multilateration (circle drawing) to find the target

Compute the distance between the target and the anchors

Select points of reference (anchors)

Range-based Localization

• Modes: – RSSI: Received signal strength indication

• Uses power loss to estimate distance (Bluetooth localization; we will see later)

– TOA: Time of arrival • Computes the time of propagation between the

transmitter and receiver (GPS; we will see later)

– TDOA: Time difference of arrival • Computes the difference between two recorded times

(Android Acoustic Ranging; we will see later)

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Range-based Localization

• Applications:

– Most localization and ranging methods:

– GPS: uses TOA

– RADAR: uses radio waves and time of flight (TDOA)

– SONAR: uses sounds waves and time of flight

• Think bats, dolphins, submarines

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Angle-based localization

• To find the target:

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Select reference points

Find directions to target

Use triangulation to find target

Angle-based Localization

• Mode:

– Direction of Arrival (also called Angle of Arrival)

• Uses arrays to find the direction to the target

• Applications

– Localization with array-based hardware

• RF antenna arrays

• Microphone arrays

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What if measurements are not accurate?

• Consider range-based localization

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Noisy measurements lead to circles not intersecting at a point

Find the centroid of the area formed

Matrix representation possible

GPS

• Global Positioning System

• One of many GNSS (global navigation satellite systems): – US: NAVSTAR GPS

– Russia: GLONASS (Globalnaya navigatsionnaya sputnikovaya sistema)

– EU: Galileo

• Other satellite navigation systems (limited coverage): – Indian subcontinent: GAGAN (GPS Aided GEO Augmented

Navigation)

– China: BeiDou

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Satellite System

• Satellites form reference points (anchors)

• 15-30 satellites in MEO (medium earth orbit):

• https://upload.wikimedia.org/wikipedia/commons/b/b4/Comparison_satellite_navigation_orbits.svg

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Calculation Steps

• A global-navigation-satellite-system (GNSS) receiver measures the apparent transmitting time from 4 or more satellites

• GNSS satellites broadcast the messages of satellites' ephemeris

• Transmitting time and the satellite location and velocity are calculated

• Range to target is estimated (based on transmit and receive time)

• Atmospheric is corrected for (some of it at least!) • Matrix problem is solved • Distribution of the location is estimated (estimate

perturbed by errors)

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Number of Satellites

• We need at least 4 satellites

• How to guarantee this?

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By Paulsava, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=47210072

GPS-Denied Localization

• GNSS works when we have access to four or more satellites in direct line of sight (LOS)

• Indoors, under forest canopies, and in urban canyons, we do not always have this

• What options do we have in such scenarios?

• Localization needs to be done with anchors in buildings

• But there are other problems as well

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The big problem - Multipath

• When we have walls, furniture, people, signals reflect off these objects and multiple copies reach the receiver

• Several copies of the

signal are received,

each staggered by a

different delay

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Effect of Multipath

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Our Solutions

• Audio – Android Acoustic Ranging

– Reflections App

• BT – RSSI-based ranging

• Testbeds – We have an Android testbed for each

– We will play with the Bluetooth solution next week

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Bluetooth-based ranging

• We use received power to estimate distances

• This method is called RSSI – received signal strength indicator

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𝑃 𝑊 = 𝛼𝑑−𝐺

Calibration

• Each anchor must act as a transmitter to all other anchors • Establish mean power between anchors • Calculate distance between each pair of anchors

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log10 𝑃 𝑊 = −𝐺 log10(𝑑) + log10( 𝛼)

Localization

• Each anchor has its own G and a values

• Each anchor estimates distance to target(s)

• Least squares algorithm used for multilateration

• Algorithm requires 3 or more anchors of known relative position

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Android Acoustic Ranging

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Acoustic Ranging Tests

• 48 kHz sampling rate = 0.7 cm distance resolution

• Indoor

– Accuracy with a variance less than 0.1 cm up to 4.5 m

• Outdoor

– Accuracy up to 50 meters

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Extension Problems

• Some solutions for indoor localization – Sequential (multi-hop)

– Using auxiliary information • Comparisons

• Multimodal

• With the data collected, what can we do? – Room characterization

– Object detection and identification

– Room shape estimation

– Continuous authentication

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Sequential Localization

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• Exploits the fact that channel effects are limited in short range

• The anchors in the blue shaded area were used to localize the green triangular nodes

• The combination of anchors and previously localized nodes in the purple shaded area are being used to localize nodes in unknown locations

• Other anchors in the network can be used to correct localization errors.

• Blue circular nodes are anchors •Green triangle nodes are at

previously estimated locations • Purple hexagonal nodes are at

unknown locations

Sequential Localization (2)

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Localization with Ordinal Distance Measurements

• Consider a set of anchor sensors, 𝓐

• A set of sensors at unknown positions, 𝓤

• Let 𝓤 ≫ 𝓐

• All sensors are on a two-dimensional plane

• The position of each sensor: 𝐱𝑙 = 𝑥𝑙 𝑦𝑙

𝑇 , ∀𝑙 ∈ 𝓐 ∪𝓤.

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Preliminary results

No assumptions on anchor locations

Anchor locations known; sensor field rotated and scaled

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Anchors are in green – crosses are actual locations and circles, after localization. Actual node locations are blue crosses and estimated locations are red circles

Room Characterization (Audio)

• Question to answer: Can we determine the characteristics of a room using audio reflections from a room?

• Using the Reflections app, data is collected from several rooms

• The clustering results suggest that an accurate K-means clustering may be performed.

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Object Detection and Classification (Audio)

• Measured data gives us distance to objects

• Can we also determine the type of object?

• Applications: Low visibility assistance

• Reflected strength and reflected impulse response can be used to determine the type of object encountered by the sound signal

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Room Shape Estimation • Consider an environment

(such as in the Figure) with n Bluetooth sources and m receivers

• Based on the received Bluetooth signal we can: – Estimate if the signal is

coming from inside the room or outside based on variance

– Estimate the distance to the Bluetooth source – make adjustment for wall if necessary

• Use distance information to build a Euclidean Distance Matrix

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Transmitter

Receiver

Wall

Wall Estimation Algorithm1

• Use multilateration to estimate location of source

• “Build” walls normal to lines joining each receiver with the source

• Select an “average” wall

• Repeat with each source

• “Construct” room

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Individual walls

Wall estimate

1I. Dokmanić, R. Parhizkar, A. Walther, Y.M. Lu, M. Vetterli, “Acoustic echoes reveal room shape,” PNAS, vol. 110, no. 30, pp. 12186-12191, 2013.

Continuous Authentication

• Static, one-time authentication is weak (e.g. password, pattern)

• Software flaws can allow bypassing of the lock screen

• Device remains vulnerable once unlocked

• Continuous authentication provides a method of ensuring user identity during use

• Valid user identity is critical in high security and high risk environments

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Continuous identity verification

• Continuously collect user gestures, keystrokes and other usage information

• Utilize machine learning to compile user data into unique profile

• Verify user profile at regular intervals with most recent behavioral metrics

• Periodically update user profile to account for habituation as user touch habits change

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Sample Activities

• Ideally, we track user behavior as they perform usual tasks

• Due to lack of (OS) permissions, we break down activities

– Shape tracing

– Image finding in a gallery

– Localization

– Typing patterns

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Preliminary Data Processing

• Collect user behavior from each activity

• Extract features from user data

• Feature selection and user profiling

• Key question:

– How long does it take to detect an unauthorized user?

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Current Research Areas

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Localization

MDS-based Sequential

Testbed

Audio-based

Bluetooth-based

Room Characterization

Object detection and

classification

Room Shape Estimation

Continuous Authentication

Core problems

Data-driven problems

Acknowledgements

• Students – Kevin Mack – Tianqi Yang – Bobby Newman – Lenoi Carter – Benjamin Robistow

• National Science Foundation – CRII – DUE

• Qualcomm Inc

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Download our app!

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Contact

Mahesh K. Banavar

Department of ECE

Clarkson University

Potsdam NY 13676

mbanavar@clarkson.edu

http://adweb.clarkson.edu/~mbanavar

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