Technical Presentation Vlt 10 12 03

2010

Ultra Wide Band Multipath Exploitation for
Anchor-less Localization and Indoor
2007
Environment Characterization
Vincenzo La Tosa

Primary Supervisor: Pr. Bernard Uguen (Univ. Rennes 1)
Co-Supervisor: Dr. Benoît Denis (CEA-LETI)

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Ultra Wide Band Multipath Exploitation for Anchor-less Localization and Indoor Environment Characterization - V. La Tosa - 03/12/10 1

Summary
 Introduction

 Problem Statement

 State-of-the-art

 Proposed Solutions with an UWB Coherent Receiver (CR)
2007

 Proposed Solution with an UWB Non-Coherent Receiver (NCR)

 Conclusion & Perspectives



Summary
 Introduction



2007





Introduction (1/3)
 IR-UWB: what is it?
• Impulse Radio – Ultra Wide Band (IR-UWB) is a low-power and short range
communication technology using a bandwidth larger than 500 MHz
• pulses are very short in time (e.g. about 1 ns for a 1 GHz bandwidth), thanks to the
very large bandwidth
Received signal, 802.15.4a channel #4, 5th interaction order allowed
1.5

1

Received tension [mV]

0.5

0

2007
-0.5

-1

-1.5
0 10 20 30 40 50 60 70 80
Time [ns]



Introduction (1/3)

 Regulation :
• different for US (FCC) and EU (ECC)
• very low Power Density Spectrum (PSD) allowed by FCC: -41.3 dBm/MHz

2007



Introduction (1/3)

 Regulation :
• different for US (FCC) and EU (ECC)
• very low Power Density Spectrum (PSD) allowed by FCC: -41.3 dBm/MHz

2007  IEEE 802.15.4a standard (2007):
• low data rate (LDR) and peer-to-peer ranging for Wireless Sensor Networks (WSN)



Introduction (2/3)

 Environment characterization: what is it?
• the characterization of a given indoor environment is the
description of :
 geometry (i.e. room map)
 materials constituting environment

2007



Introduction (3/3)

 Anchor-less localization: what is it?
• for a given radio device the capability of getting aware of its
coordinates in a local and relative Cartesian coordinate
system in the surrounding environment
• no node can be an anchor (i.e. reference node)

y

y2
2007

y1
x
x1 x2



Summary
 Introduction



2007





Problem statement
 Joint environment characterization and anchor-less localization
while communicating

2007



Problem statement
while communicating

 The two SISO nodes belonging to a WSN exchange packets

2007



Problem statement
while communicating
Dx

y2
Dy
y1
x1 x2

2007  Thanks to multi-path diversity, the receiver can estimate room
dimensions and nodes coordinates in the room



Problem statement
while communicating
Dx

y2
Dy
y1
x1 x2

2007  Thanks to multi-path diversity, the receiver can estimate room
dimensions and nodes coordinates in the room

 Application fields: robotics, home automation, home entertainment,
mobility in dangerous environments



Summary
 Introduction



2007





State-of-the-art
 Environment characterization:
 UWB Imaging (R. Zetik, 2005)
 UWB monostatic radar technique or Single Input Multiple Output (SIMO) transmission (i.e. multiple
receiving antennas) in a mobile scenario, providing an image of the environment and exploiting
collected Channel Impulse Responses (CIR)

2007



State-of-the-art
receiving antennas) in a mobile scenario, providing an image of the environment starting from

 Simultaneous Localization And Mapping (SLAM) (J. A. Castellanos,1997)
 mobile robots discovering the indoor environment without radio communications (e.g. through
camera, laser)

2007



State-of-the-art

camera, laser)

 Joint communications, localization and environment characterization:
 Indoor Mapping (W. Guo, 2005)
 geometric relationships linking the differences of Times of Arrival with geometry
2007



State-of-the-art

camera, laser)

 Joint communications, localization and environment characterization:
 Indoor Mapping (W. Guo, 2005)
 geometric relationships linking the differences of Times of Arrival with geometry
2007

 Radiolocation with anchors:
 Different solutions (S. Gezici, 2005)
 AoA, Signal Strength, ToA



Described Investigations

 Two different approaches:

 Coherent Receiver (CR)
 high frequency sampling (i.e. Nyquist compliant, fs = 15 GHz)
 floating point dynamic range for signal representation

2007



Described Investigations

 Two different approaches:

 Coherent Receiver (CR)
 high frequency sampling (i.e. Nyquist compliant, fs = 15 GHz)
 floating point dynamic range for signal representation

 Non-Coherent Receiver (NCR)
 low frequency sampling (i.e. much lower than Nyquist frequency)
2007  4-bits dynamic range for signal representation



Common Environment Model
 Indoor environment model:
 rectangular room with walls numbered anticlockwise
 for the reflected paths, the label of parameters corresponds to that of the
interacting wall
 Angles of Incidence (AoIs) defined as the angle involved in the reflection

2007

 , 
1 tx,1 , rx ,1, 1 



interacting wall

2007

tx ,1

 , 
1 tx,1 , rx ,1, 1 



interacting wall

rx,1

2007

 , 
1 tx,1 , rx ,1, 1 



interacting wall

 ,  , rx , 2 ,  2 
2007

2 tx, 2

 , 
1 tx,1 , rx ,1, 1 



interacting wall

 , 
3 tx,3 , rx ,3 ,  3 

 ,  , rx , 2 ,  2 
2007

2 tx, 2

 , 
1 tx,1 , rx ,1, 1 



interacting wall

 , 
3 tx,3 , rx ,3 ,  3 

 ,  , rx , 4 ,  4   ,  , rx , 2 ,  2 
2007

4 tx, 4 2 tx, 2

 , 
1 tx,1 , rx ,1, 1 



Summary
 Introduction



2007





Signal Model

 Single path in the frequency domain:

 generic received path:

2007



Signal Model



2007



Signal Model



 antennas filter the pulse feeding the transmitting antenna, depending on
DoD ( ) and DoA ( ), in addition to path channel

2007



Signal Model



 antennas filter the pulse feeding the transmitting antenna, depending on
DoD ( ) and DoA ( ), in addition to path channel

2007
 Tension of received multipath signal in the time domain:
 corresponds to the sum of received paths

 with filtered white Gaussian noise and


CR approach (1/2)
 Principles:
 a database of templates can be locally built thanks to prior antenna
characterization and relying on the previous deterministic model

2007



CR approach (1/2)
 Principles:

2007 Templates for direct paths
and
single-bounce paths
Templates indexed
by an AoA/AoD
and/or AoI



CR approach (1/2)
 Principles:
 a good representation (i.e. floating point) of the received signal is available

Received tension only considering direct path and single-bounce paths
1

0.5
2007

0

-0.5

-1
0 5 10 15 20 25 30 35
Time [ns]



CR approach (1/2)
 Principles:
 a good representation (i.e. floating point) of the received signal is available
 the received multipath components are assumed to be detectable thanks to
the intrinsic UWB time resolution (i.e. path-by-path approach)

1

0.5
2007

0

-0.5

-1
0 5 10 15 20 25 30 35
Time [ns]



CR approach (2/2)
 Principles:
 each received multipath component is associated to one or more
templates through cross-correlation
 each received multipath component is associated with a set of
transmission parameters (i.e. indexing the local database of templates)

Multipath component cross-corr
1 2007

0.5

0

-0.5

-1
0 5 10 15 20 25 30 35
Time [ns]



CR approach (2/2)
 Principles:
 each received multipath component is associated to one or more
templates through cross-correlation
 each received multipath component is associated with a set of
transmission parameters (i.e. indexing the local database of templates)

Template(s) Transmission
Multipath component cross-corr
parameters
1 2007

0.5
Param a
0

Param b
-0.5
a
-1
b
0 5 10 15 20 25 30 35
Time [ns]



CR approach

 Principles illustration:

Cross-correlation 1
with the local
database of
predicted

0.5
waveforms

0

-0.5

   0  ,   0  ,  0  
-1
2007 0 5 10 15 20 25 30 35
Time [ns]



CR approach


Cross-correlation 1
with the local
database of
predicted

0.5
waveforms

0

-0.5

   0  ,   0  ,  0  
-1
2007 0 5 10 15 20 25 30 35
Time [ns]



CR approach


1 Cross-correlation
with the local
database of
predicted

0.5
waveforms

0

-0.5

   0  ,   0  ,  0      ,   ,   ,   
-1
2007 0 5 10 15 20 25 30 35 1 1 1 1
Time [ns]

   ,   ,   ,      , 
1 1 1 1
1 tx ,1 , rx ,1 ,  1 

Detection order  Model order


CR approach


1 Cross-correlation
with the local
database of
predicted

0.5
waveforms

0

-0.5

   0  ,   0  ,  0      ,   ,   ,   
-1
2007 0 5 10 15 20 25 30 35 1 1 1 1
Time [ns]

????

Label mapping ambiguity



CR approach
 The proposed algorithms aim at linking the available sets of
transmission parameters to a couple of nodes coordinates and an
environment geometry

   ,   ,   
0 0 0

 
1
Dx
 1 ,  1 ,  1 , 1

0.5

   ,   ,   ,   
y2
0 2 2 2 2
Dy

   ,   ,   ,   
-0.5
3 3 3 3
y1
-1 x1 x2

   ,   ,   ,   
0 5 10 15 20 25 30 35
Time [ns]
4 4 4 4
2007



CR approach
 The proposed algorithms aim at linking the available sets of
transmission parameters to a couple of nodes coordinates and an
environment geometry

   ,   ,   
0 0 0

 
1
Dx
 1 ,  1 ,  1 , 1

0.5

   ,   ,   ,   
y2
0 2 2 2 2
Dy

   ,   ,   ,   
-0.5
3 3 3 3
y1
-1 x1 x2

   ,   ,   ,   
0 5 10 15 20 25 30 35
Time [ns]
4 4 4 4
2007

 The error on  and   depends on the antenna radiation pattern



Tree algorithm for the CR case (1/3)
 Transmission parameters (i.e AoIs, AoAs, AoDs and ToAs) are
considered as measurements, related to an hidden state
• nodes coordinates and room main dimension are the hidden state
variables

Hidden state (i.e. coordinates
Parameters measurements
and room dimension)

   ,   ,   
0 0 0
Dx

   ,   ,   ,   
1 1 1 1 y2
Dy

   ,   ,   ,   
2007 2 2 2 2 y1
x1 x2
   ,   ,   ,   
3 3 3 3

   ,   ,   ,   
4 4 4 4



variables

Hidden state (i.e. coordinates
Parameters measurements Estimation tree and room dimension)

   ,   ,   
0 0 0
a …
Dx

 , , , 
a b a
1 1 1 1 a b c … b y2
c d c Dy

   ,   ,   ,   
2007 2 2 2 2 b d e … d
f
y1
e
x1 x2
   ,   ,   ,   
3 3 3 3

Likelihood-oriented
   ,   ,   ,   
4 4 4 4



variables

 For instance, the true value of the AoI labeled as 1, according to
the wall numbering in the model, is bound up with nodes
coordinates as follows:

2007

and the true distance between nodes is simply:



variables

 For instance, the true value of the AoI labeled as 1, according to
the wall numbering in the model, is bound up with nodes
coordinates as follows:

2007

and the true distance between nodes is simply:

 The solution detailed in the following does not exploit AoAs and
AoDs but only AoIs and ToAs (i.e. MAL, basic algorithm)


 Assuming a Gaussian centered noise affecting measurements, a
likelihood function can be easily built for each path label mapping

2007

 4
All the    4 configurations are equally probable a priori
1




 The path label mapping ambiguity is solved using Averaged Likelihood
Functions (ALFs)
 ALFs are built as mixtures of likelihood functions, weighting equally each path label
mapping hypothesis

2007




 The path label mapping ambiguity is solved using Averaged Likelihood
Functions (ALFs)
 ALFs are built as mixtures of likelihood functions, weighting equally each path label
mapping hypothesis

2007

 ALFs are maximized over nodes coordinates and room main
dimension

Maximum Averaged Likelihood (MAL) algorithm


 An extension of MAL algorithm is obtained including AoA and AoD
“measurements” (i.e. still through cross-correlation) in the averaged
likelihood
 extended MAL (eMAL)

 Both algorithms share a tree approach

1
Param a
2007

0.5

0

-0.5

-1
Param b
0 5 10 15 20 25 30 35
Time [ns]



Technical Presentation Vlt 10 12 03

Technical Presentation Vlt 10 12 03

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