Indoor Localization without Infrastructure using the Acoustic

S. P. Tarzia - P. A. Dinda - R. P. Dick - G. Memik

Indoor Localization without Infrastructure using the

Acoustic Background Spectrum

Advanced Computer Architecture - Politecnico di Milano - 2012

Vincenzo Baldini - Matteo Torcoli

Presentation outline

● starting problem: localization● ABS concepts● how to get a good ABS fingerprint?● accuracy issues● an implementation for mobile phone● putting the application to the test● future works

reference: http://stevetarzia.com

Indoor Localization

i.e. which room am I in? ● outdoor localization -> well solved by GPS ● indoor localization

without an infrastructure specifically deployed for this scope?

Wi-Fi localization

fingerprint = WiFi signals intensity

fingerprint -> roomlabelroom "1"

room "3"

room "2"

room "4"

Beyond Radio

fingerprint -> roomlable

fingerprint = Acoustic Background Spectrum

● always available resource● surprisingly distinctive! ● compact and easily computed● robust

ABS Goalindependently from any specialized hw

Allow even a basic mobile device to

cheaply and quickly determine its location

by matching

previously-learned, specific location labelsto recordings

2 steps

1. Calculate the room's ABS2. Room's Classification

1. ABS fingerprint extraction

● Record and Windowing● Power Spectrum (through FFT)● Filter the freq. band of interest● Reject transient noise

Transient noise rejection

DistinctiverEsponsive Compact Efficiently-computable Noise-robust Time-invariant

Filter out transient

components

HOW?

5th-percentile value

(P05) for each frequency

ABS fingerprint extraction

2. The classification problem

to label the room

DISTANCE METRIC

VECTOR EUCLIDEAN DISTANCE

Current ABS room fingerprint compared with

previously-observed ones

● training pairs ● testing fingerprint● the classifier chooses:

Trace collection and simulation

Simulation to evaluateABS-localization accuracy

● 33 rooms in the Northwestern University● 30 seconds mono recordings

(24 bit - 96 kHz wav files)● 2 visits in different weeks ● 4 positions per room● Zoom H4n handheld recorder:

ABS from 2 rooms using the optimal parameter values

Simulation results

Confusion matrix for the 33 room simulation

(optimal values)

Noise-robustness problem (noisy room samples)

- Recordings in a lecture hall before, during and after lectures - 3 occupancy noise states:

1. quiet times - in the training set 2. conversation times3. chatter times

- in the test set

AUTOMATIC BAND SWITCHING

EXPERIMENT: frequency bands and occupancy noise

How to improve the accuracy

different fingerprint distances in a linear combination:● wi-fi● cellular radios ● accelerometer● camera data● ABS...

● = distance for each fingerprint types● = weighting constant● = match range

Mobile application: BatphoneMain features:

● it's free!● localization in real-time● capture and export fingerprints● ABS compact fingerprint + Wifi coordinates● almost the same extraction steps

Apple Ipod Touch (iOS 4.0 or later)

with the Batphone's GUI

Hardware limitations:1. microphone (speech frequencies)2. computation power (=> rectangular windows)3. memory (=> compact fingerprints)4. battery

Batphone experiment

● Ipod Touch 4g● 43 rooms ● 2 room positions =>● less training data ● fingerprint = ABS + Wifi

Neverthless good results!

Our Batphone experiment

* Strong change in conditions** Chatter problem

*

* *

Future works

fingerprint sharing/matching infrastructures - online DB

others kinds of fingerprint: - camera data

- accelerometer..

chatter problem: an open issue

automatic switching of frequency bands