channel context detection and signal quality monitoring

ION GNSS 2010, Session B4, Portland, OR, 21 – 24 September 2010 1/14

Channel Context Detection and Signal Quality

Monitoring for Vector-based Tracking Loops

Tao Lin, Cillian O’Driscoll and Gerard Lachapelle

Position Location And Navigation Group

Department of Geomatics Engineering

Schulich School of Engineering

University of Calgary

BIOGRAPHY

Tao Lin is a Ph.D. candidate in the PLAN Group of the

Department of Geomatics Engineering at the University

of Calgary. He received his BSc. from the same

department in May 2008. His research interests include

the fields of GNSS software receiver design, digital signal

processing, satellite-based navigation, inertial navigation

and ground-based wireless location.

Dr. Cillian O’Driscoll received his Ph.D. in 2007 from

the Department of Electrical and Electronic Engineering,

University College Cork. His research interests are in the

area of software receiver for GNSS, particularly in

relation to weak signal acquisition and ultra-tight

GPS/INS integration. He is currently with the Position,

Location And Navigation (PLAN) group at the

Department of Geomatics Engineering in the University

of Calgary.

Dr. Gérard Lachapelle is a Professor of Geomatics

Engineering at the University of Calgary where he is

responsible for teaching and research related to location,

positioning, and navigation. He has been involved with

GPS developments and applications since 1980. He has

held a Canada Research Chair/iCORE Chair in wireless

location since 2001.

ABSTRACT

Vector-based tracking has attracted a significant amount

of attention over the past two decades due to its capability

of weak signal tracking in urban canyon environments

where some GNSS signals are highly attenuated or

blocked. Although a large amount of research work has

been published on the implementation and performance

evaluation of vector-based tracking loops, little research

has been conducted on the reliability analysis of vector-

based tracking loops. The impetus for this paper and

underlying research is to develop a suitable signal

monitoring and channel context detection algorithm for

vector-based tracking loops. In this paper, two new

approaches, i.e. signal compression and fading parameter

(e.g. the Ricean K-factor) monitoring, are applied to

GNSS signal monitoring and channel context detection.

Three categories of Ricean K-factor estimators are

introduced: envelope-based, envelope/phase-based and

phase-based. Their performance, limitations, and practical

implementation challenges in a high sensitivity GNSS

receiver are investigated.

INTRODUCTION

The optimal choice of processing strategy and parameters

in a GNSS receiver is a function of many factors: the

strength of the signal, the LOS dynamics, and the signal

environment, e.g. open sky vs. urban canyon. There is,

therefore, a desire to develop a new GNSS receiver

architecture that is able to determine and classify these

environmental contexts and adjust its processing

strategies and parameters accordingly. Herein such a

receiver is referred to as context aware.

GNSS signals are usually processed satellite-by-satellite

using scalar-based tracking loops. The benefits of scalar-

based tracking are the relative ease of implementation and

a level of robustness that is gained by not having one

tracking channel corrupting another tracking channel.

However, the fact that the signals are related via the

receiver’s position and velocity is completely ignored

(Petovello et al. 2006). In contrast to scalar-tracking,

vector-based tracking combines the signal processing and

the navigation solution into one step so that one tracking

channel can aid other channels via the estimated

receiver’s position, velocity, clock bias and clock drift,

where some, but not all, of the GNSS signals in view are

highly attenuated or blocked (Petovello et al. 2008).

However, because of the aiding among channels, one

tracking channel can corrupt other tracking channels in


vector-based tracking. Hence signal/channel monitoring is

necessary.

In this paper, the focus is on two new signal/channel

monitoring approaches in the signal domain, namely

signal compression and fading parameter (e.g. the Ricean

K-factor) monitoring. This paper begins with the

description of the signal and receiver models used in this

research followed by the theory and the implementation

details of signal and channel monitoring using

compressed signal waveform or fading parameter

estimation. Finally, some experimental results based on

GPS L1 C/A signals are presented to validate the methods

introduced.

SIGNAL AND RECEIVER MODELS

In this section, the signal and the receiver models used in

this research for GNSS signals in signal degraded

environments are introduced.

Signal Model

A Gaussian channel model is typically used to model

open-sky environments. The prompt correlator output can

be expressed as follows:

( )( ) ( )sin

kjs kk k k k k k

s k

NT fP A R d e n

NT f

φπτ

π

∆∆= ∆ +

∆ (1)

where kP is the prompt correlator value at the k th dump

epoch , kτ∆ is the code phase error, kA is the LOS signal

amplitude, kd is the navigation data, kR is the spreading

code correlation value, kf∆ is the Doppler frequency

error, kφ∆ is the carrier phase error, sT is the sample

period, N is the number of coherent integration samples,

and kn is a sample from an additive white Gaussian noise

(AWGN) process.

In a multipath environment, assuming M signal paths

exist, the first path corresponds to the Line-Of-Sight

(LOS) signal while the remaining 1M − paths correspond

to Non-Line-Of-Sight (NLOS) signals. If the coherent

integration time is shorter than the coherence time of the

propagation channel, the prompt correlator output can be

expressed as follows:

( )( ) ( )

( )( ) ( )

,0

,

,0

,0 ,0 ,0,0

1,

, , ,,1

sin

sin

k

k i

k k k k

s k j

k k k k k

s k

Ms k i j

k k k i k i k i

s k ii

k

P S M n

NT fA d a R e

NT f

NT fA d a R e

NT f

n

φ

φ

πτ

π

πτ

π

∆

−∆

=

= + +

∆= ∆

∆

∆+ ∆

∆

+

∑ (2)

where kS is the LOS signal component at the k th dump

epoch, kM is the multipath or NLOS signal component,

and ,k ia is the multipath path attenuation.

After factoring out the LOS signal component from the

NLOS signals component, and assuming the relative

delays are small relative to the chip length and the relative

Doppler differences are small relative to the correlator

dump rate, then Equation (2) above can be rewritten as

1

,

0

k k k k

M

k i k k

i

k k k

P S M n

h S n

H S n

−

=

= + +

= • +

= • +

∑ (3)

where ,k ih is a complex coefficient which represents the

channel gain on the i th path at the k th

dump epoch and

kH is the total channel gain at the k th dump epoch.

If the number of multipath signals approaches infinity and

the angle of arrival of the multipath signals are uniformly

distributed from 0 to 2π , the multipath component kM

becomes a complex Gaussian random variable (Nielsen et

al. 2009). Therefore, the complex channel gain becomes a

non-zero mean complex Gaussian process, and the

envelope of the prompt correlation follows Ricean

distribution.

Although multipath has constructive and destructive

effects, sometimes it is convenient to model the multipath

fading by a complex ‘attenuation’ term. An alternative

form of the signal model shown above is as follows

(Schmid et al. 2005):

( )( ) ( )sin

2 kjs kk k k k k

s k

k

NT fP Cd R e v

NT f

n

φπτ

π

∆∆= ∆

∆

+

(4)

where C is the total received signal power (both LOS and

NLOS signals) and kv is complex fading attenuation due

to the NLOS signals at the k th dump epoch.


The fading attenuation kv is a non-zero mean complex

Gaussian process. Its envelope follows the Ricean

distribution (Schmid et al. 2005). Since the received

signal power C has been factored out, the ratio between

the deterministic LOS signal power component and the

NLOS signal power component is defined as the Ricean

K-factor:

2

2

v

v

AK

σ= (5)

where { }v kA v= Ε and { }22v k vv Aσ = Ε − .

Given 2 2 1v vA σ+ = , the LOS power and the NLOS power

can be expressed as a function of the Ricean K-factor.

2

1v

KA

K=

+ (6)

2 1

1v

Kσ =

+ (7)

The Ricean fading model is the generalization of both the

Gaussian model, which is typically used in outdoor GNSS

channel modeling, and the Rayleigh fading model, which

is commonly used in mobile communication. As the

Ricean K-factor approaches infinity, the Ricean fading

model reduces to the Gaussian model. If the Ricean K-

factor is zero, the Ricean fading model is equivalent to the

Rayleigh fading model. Although the Ricean fading

model above might not be the exact propagation channel

model for GNSS signals, it has been used successfully for

weak and faded GNSS signal acquisition in HS-GNSS

receivers (Schmid et al. 2005). Therefore, in this research

the Ricean fading model is used to model the GNSS

propagation channel over short durations.

Receiver Model

Based on the closure of the local channel feedback loops

and the navigation feedback loop, all existing GNSS

signal tracking schemes can be categorized into four

groups, namely scalar-based tracking, decentralized

vector tracking, centralized vector tracking and open-loop

tracking.

Scalar-based tracking is the most standard tracking

scheme. It closes the local channel tracking loops only. In

a decentralized vector-based tracking loop, the loop is

closed through the navigation solution, with local filters

in each channel performing range and range rate

estimation. In a centralized vector-based tracking loop,

the raw discriminator outputs are directly passed into the

navigation filter and only the navigation feedback loop is

closed to track all signals. Pany & Eissfeller (2006)

reported that the centralized structure provides a better

sensitivity than the decentralized structure. The major

drawbacks of centralized vector-based tracking are: i) the

highly noisy filter input, which comes from the raw

discriminator output and ii) the asynchronous nature

between the local channel-epoch and the navigation

measurement-epoch. Open-loop tracking was firstly

introduced to GNSS by van Graas et al. (2005). Although

it requires a lot more processing power compared to the

other tracking schemes, it provides higher robustness to

track weak and faded signals than closed-loop tracking,

since the reduction of the loop filter update rate due to the

longer coherent and/or non-coherent integration will

cause a tracking loop to be unstable if the tracking loop

was not optimized or re-designed for a low update rate

(Kazemi 2009).

A generic GNSS tracking model is shown in Figure 1. In

this figure, the black solid lines represent the fixed

processing chain while the red dashed lines represent the

adjustable processing chain. Based on the closure of the

local channel feedback loops and the navigation feedback

loop (see Table 1), this generic tracking loop can

transform to any of the tracking schemes mentioned

above.

To improve the robustness, integrity and performance of

vector-based tracking, or even to switch from a

conventional vector-based tracking scheme to other

schemes, the quality of each processing channel and the

type of propagation channel need to be quantified and

detected, which indeed are the core tasks in this research.

Figure 1 A generic GNSS signal tracking model


Table 1 The closures of GNSS signal tracking schemes

Navigation

Feedback Loop

Channel

Tracking Loop

Scalar-based

Tracking Open Closed

Decentralized

Vector-based

Tracking

Closed Partially closed

Centralized Vector-based

Tracking

Closed Open

Open-loop

Tracking Open Open

CONVENTIONAL SIGNAL AND CHANNEL

MONITORING

Signal/channel monitoring can be performed at the

navigation ranging level and the signal level. At the

navigation ranging level, blunder detection and removal is

usually implemented based on residual analysis.

However, the difficulty of blunder detection increases

dramatically with a decrease in the number of available

measurements. In the signal domain, monitoring is

usually based on C/N0, Phase-lock-indicator (PLI) and

frequency-lock-indicator (FLI). PLI and FLI are not easy

to use for measurement weighting and propagation

channel monitoring because these two quantities are

tracking scheme dependent. C/N0 and satellite elevation

can be used to weight the measurements; however, the

multipath fading impact (especially the multipath

constructive effect) cannot be reflected directly using this

approach.

There are two new approaches on GNSS signal and

channel monitoring. The first approach is to monitor the

spreading code chip shape by signal compression

technology while the second method is to estimate the

fading channel parameters, such as the Ricean K-factor.

These two new methods are introduced in the next two

sections.

CHANNEL MONITORING WITH SPREADING

CODE CHIP SHAPE

Signal compression technology detects and observes

multipath signals at the chip level (Weill 2007). In signal

compression, a large number of baseband signal samples

(chips) are coherently summed to appear as one single

PRN code chip of the received signal. Only simple

additions are required to generate and preserve all signal

information. If the number of signal samples used for

signal compression is sufficiently large, the processing

gain of compression is great enough to make the

compressed signal visible with little noise in a way

similar to the processing gain of coherent integration so

that small subtleties in the compressed chip waveform due

to front-end filtering, multipath or other distortion can

easily be seen (Weill 2007). Fenton & Jones (2005) and

Weill (2007) have shown that multipath signals are easier

to observe and mitigate at the chip level than the

correlation level. In Figure 2, the normalized compressed

chip shape of the live GPS L1 C/A signals from a front-

end with 10 MHz bandwidth and a front-end with 5 MHz

bandwidth are plotted together with the simulated perfect

chip shape (with infinite front-end bandwidth). As shown

in Figure 2, the font-end filtering impact on the

compressed chip shape is easy to observe.

Figure 2 Front-end filtering impacts on chip shape

To further access the performance of signal compression

in signal monitoring, a data collection was conducted with

a static antenna in front of the CCIT building at the

University of Calgary (see Figure 3).

Figure 3 Experiment environment

The C/N0 values of faded signals from a low elevation

satellite (PRN 07) are plotted in Figure 4. Three time

spans associated with 15 dB, 10 dB and 8 dB fading are

identified. The compressed chip shape for each case is

generated by coherently accumulating 2 s of baseband

samples after phase-lock. The chip shape of the faded


signal is compared to that of the LOS signal with the

same front-end in Figure 5, Figure 6, and Figure 7.

Figure 4 C/N0 of faded signals at PRN 7

Figure 5 Spreading code chip shape for 15 dB fading



From the plots above, the multipath distortion on chip

shape for the 15 dB and the 10 dB cases are obvious, but

the chip distortion for the 8 dB case is not very obvious.

The chip shape distortion probably is caused by a LOS

signal and an out-of-phase multipath signal sum

destructively.

Although signal compression can be used for

signal/channel monitoring in outdoor environments,

several factors limit the use of such technique in a HS-

GNSS receiver. First of all, because baseband samples are

needed to form a coherently integrated chip, a closed-loop

phase tracking loop is required; thus it cannot be used

with snapshot-based HS-GNSS receivers, which usually

do not track signals in closed-loop sense. Secondly it only

shows the multipath impact at the chip domain or the

ranging domain. Thirdly, the multipath impact in Doppler

domain is totally ignored. Finally, the most limiting factor

is that the multipath has to be deterministic or static

during the coherent sum of the baseband samples (i.e. 2 s

for the results shown above) to form a chip.

As mentioned in the previous section, the nature of

multipath tends to be random rather than deterministic as

the number of multipath increases (i.e. indoor or semi-

indoor environments). If the antenna is in a kinematic

mode instead of a static mode, multipath cannot be ‘static’

or deterministic during the time interval for chip shape

generation. Also in highly signal degraded environments,

phase-lock with a PLL is hard to achieve. Thus an

alternative approach is needed for heavy fading

environments such as indoors.

CHANNEL MONITORING WITH FADING

PARAMETERS

In wireless communications, adaptive modulation

technology has been applied recently to increase the data

rate and maintain low bit error rate (BER) by monitoring

and predicting communication links and changing the


modulation scheme adaptively. One of the key metrics

typically used in adaptive modulation technology for

evaluating communication links is the signal fading level,

which can be measured by the Ricean K-factor.

Ricean K-factor estimators generally can be categorized

into three groups: envelope-based estimators,

envelope/phase-based estimators, and phase-based

estimators. Six Ricean K-factor estimators and their

theoretical performance are briefly introduced in this

section.

Envelope-based Ricean K-factor Estimators

As shown by Tepedelenlioglu et al. (2003), at least two

different moments are required to estimate the Ricean K-

factor with envelope information only. The n th moment

can be estimated by averaging in a moving window

of N samples as:

1

0

1ˆ .

Nn

n i

i

rN

µ−

=

= ∑ (8)

Suppose n m≠ , the function ( ),n mf ⋅ and its inverse

function ( )1,n mf− ⋅ are defined as (Tepedelenlioglu et al.

2003):

( ), : .mn

n m nm

f Kµ

µ= (9)

1, ,

ˆˆ :

ˆ

mn

n m n m nm

K fµ

µ

−

=

(10)

The common choices for ( ),n m are ( )1, 2 and ( )2, 4 .

The corresponding ( ),n mf ⋅ functions are as follows

(Tepedelenlioglu et al. 2003):

( )( )

( )

( )

2

1, 2 0 1

2

14 1 2 2

1

Ke K Kf K K I KI

K

g K

K

π − = + +

+

= +

(11)

( )( )

22

2, 4 2

1

4 2

Kf K

K K

+ = + +

(12)

To estimate the Ricean K-factor, Equation (11) or (12)

need to be inverted. As shown by Azemi et al. (2003),

( )g K in Equation (18) can be approximated by a linear

or a quadratic polynomial with coefficients computed by

curve fitting:

( )1 1 0

1 01.000, and 0.7513

g K a K a

a a

= +

= = (13)

( ) 22 2 1 0

92 1 08.3285 10 , 1.000, and 0.7527

g K b K b K b

b b b−

= + +

= × = =

(14)

Therefore the Ricean K-factor can be estimated with 1st

and 2nd

moments based on a first order and a second order

approximation as follows (Azemi et al. 2003):

st

10

21, 2,1

11

2

ˆ

ˆˆ

ˆ

ˆ

a

K

a

µ

µ

µ

µ

−

=

−

(15)

nd

2

1 1 11 1 2 0

2 2 2

1, 2, 22

ˆ ˆ ˆ4

ˆ ˆ ˆˆ .

2

b b b b

Kb

µ µ µ

µ µ µ

− + − + −

=

(16)

Since 0K ≥ , from Equation (12), the Ricean K-factor

can be estimated with 2nd

and 4th

moments

(Tepedelenlioglu et al. 2003):

2 22 4 2 2 4

2, 4 22 4

ˆ ˆ ˆ ˆ ˆ2 2ˆ

ˆ ˆK

µ µ µ µ µ

µ µ

− + − −=

− (17)

Envelope/Phase-based Ricean K-factor Estimators

In some applications, coherent tracking is possible; thus

both phase and envelope information is available for

Ricean K-factor estimation. As shown by Chen &

Beaulieu (2005), the probability density function of the

fading envelope and fading phase is given by:

( )( )2 2

0, 2 2

2 cos, expr

r A rArp rθ

θ θθ

πσ σ

+ − − = −

(18)

where 0θ is the LOS phase (assuming to be constant

during averaging), r is the envelope of signals, θ is the

phase of signals, A is the LOS signal amplitude, and 2σ is the multipath power.


Assuming N independent and identically distributed

fading channel samples are available, the maximum

likelihood estimator (MLE) for Ricean K-factor can be

obtained by maximizing the log-likelihood function. Chen

& Beaulieu (2005) derived the MLE as follows:

{ }

{ }

1 11 10

1 1

sin

ˆ tan tan

cos

N N

k k k

k k

N N

k k k

i i

r P

r P

θ

θ

θ

− −= =

= =

ℑ

= =

ℜ

∑ ∑

∑ ∑ (19)

( ) { }0ˆ

0

1 1

1 1ˆ ˆcos

N Nj

k k k

k k

A r P eN N

θθ θ −

= =

= − = ℜ∑ ∑ (20)

2 2 2 2

1 1

1 1ˆ ˆˆN N

k k k

k k

r A P P AN N

σ ∗

= =

= − = −∑ ∑ (21)

2

2

ˆˆ

ˆMLE

AK

σ= (22)

where 0θ̂ is the estimated LOS phase (assuming to be

constant during averaging), kr is the envelope of signals,

kθ is the phase of signals, kP is the prompt correlation,

and N is the number of samples for averaging.

As shown by Baddour & Willink (2007), the MLE has a

bias of { } ( ) ( )ˆ 2 1 2MLE

K K K NΕ − = + − , but it

becomes asymptotically unbiased when a large number of

samples are used for averaging. Hence an unbiased

version of the MLE for finite samples:

( )1ˆ ˆ2 1MML MLK N KN = − −

(23)

where N is the number of samples for averaging.

Phase-based Ricean K-factor Estimators

By integrating the envelope argument, the probability

density function with only phase argument can be shown

as (Chen & Beaulieu 2005):

( )( ) ( )

( )( )

20sin0

0

cos

2 2

erfc cos

KKKe

p e

K

θ θθ

θ θθ

π π

θ θ

−− −−

= +

⋅ − −

(24)

where ( )erfc ⋅ is the complementary error function.

The approximate MLE which maximizes the log-

likelihood function for relatively large K is as follows

(Chen & Beaulieu 2005):

( )20

1

ˆ

ˆ2 sin

AML N

i

k

NK

θ θ=

=

−∑

(25)

where 0

1

1ˆN

k

kN

θ θ=

= ∑

Theoretical Performance of the Ricean K-factor

Estimators

To access the performance (the bias and the standard

deviation) of the estimators introduced above at different

C/N0 and K-factor values, a Monte-Carlo simulation was

done at the correlation level with a moving window of

100 samples (correlation values). In this simulation,

additive white Gaussian noise (AWGN) and Ricean

fading were added into the deterministic LOS signals. The

LOS signal phase was set to be a constant; the signal

Doppler and the spatial correlation between consecutive

samples were ignored.

From Figure 8 to Figure 11, the bias and the standard

deviation values for all estimators are plotted in solid

lines and dashed lines respectively with various C/N0

values, K-factor values and coherent integration times. A

negative bias appears for all estimators when the post-

correlation SNR is low due to low C/N0 and/or short

coherent integration, and the value of K-factor is

relatively large. Since the model used in the K-factor

estimation does not include the impact of AWGN, this

negative bias represents the impact of AWGN on the K-

factor estimation. If the post-correlation SNR is low while

the K value is relative large, meaning the level of AWGN

compared to Ricean fading is large; AWGN has

significant impact on the variation of the prompt

correlation’s envelope, which cannot be neglected.

Therefore the estimated K-factor is smaller compared to

the true value. It can be also observed that the standard

deviation increases as the K-factor increases. Similar to

the results from Chen & Beaulieu (2005) and Baddour &

Willink (2007), the MML and the MLE outperform

others. However, the difference is not large at all,

especially for GNSS signal/channel monitoring. One

point to bear in mind is that any phase-based K-factor

estimator assumes that the LOS signal phase is a constant

while the variation of the phase estimate is due to the

NLOS signals. Since the actual LOS phase of GNSS

signals is not a constant due to the motion and instability

of oscillators, phase tracking or highly precise frequency

tracking in a short duration is required to maintain a


‘stable’ phase. However the residual or the variation of

this ‘stable’ phase is not due to multipath only but the net-

effect of many other factors such as motion. Also a higher

post-correlation SNR is required for precise phase

estimation than for envelope estimation. Therefore

envelope based estimators are more robust and easier to

use than the others since they only require the envelope

information, which are available in any type of GNSS

receivers.

Figure 8 Performance of K-factor estimators with 100

ms coherent integration at 35 dB-Hz

Figure 9 Performance of K-factor estimators with

1000 ms coherent integration at 35 dB-Hz





SIGNAL MONITORING AND CHANNEL

CONTEXT DETECTION IN THE REAL WORLD

In the last two sections, signal compression and fading

parameters monitoring were introduced for signal/channel

monitoring. In this section, the results and analyses from

two experiments are presented. The first experiment was

conducted to assess the applicability and the consistency

of the Ricean K-factor estimation for GNSS signals. The

main purpose of the second experiment is to validate the

performance of the signal compression and fading

parameter estimation approaches for signal monitoring

and channel context detection in practice.

Experiment1: K-factor indoors and outdoors

To assess the performance of the K-factor estimators

indoor and outdoor with live GNSS signals (GPS L1 C/A

signals in this case), a static antenna was placed on the top

of a wooden house while another static antenna was in the

house. An NI front-end was used to collect data from both

channels at the same time. The software GNSS receiver


developed at the University of Calgary, GSNRxTM

(O’Driscoll et al. 2009), was used to process the data. In

both cases, phase-lock can be maintained.

The estimated C/N0 and K-factor outdoors and indoors are

shown from Figure 12 to Figure 15. As shown in these

plots, the K-factor can clearly reflect the signal strength

difference between indoor and outdoor. Although the

estimates from each group of estimators (i.e. envelope-

based) match well, a bias surprisingly exists between any

two groups of estimators. In other words, the fading

statistics from the envelope and the phase are not exactly

the same. As discussed in the previous section, the

residual phase, which is the tracking jitter of a PLL or the

accumulated tracking jitter of a FLL, is due to not only

multipath but also noise, signal dynamics, oscillator

instability, and tracking loop performance. In addition,

signal phase estimation is highly affected by noise.

Therefore an envelope-based K-factor estimator seems to

be a better choice for a GNSS receiver. In order to

understand this bias or validate the thought above, more

experiments and analyses are needed.

Figure 12 C/N0 from a static antenna outdoor

Figure 13 K-factor from a static antenna outdoor

Figure 14 C/N0 from a static antenna indoor

Figure 15 K-factor from a static antenna indoor

Experiment2: Channel Monitoring and Context Detection from Outdoor to Indoor

In order to validate the performance of signal

compression and fading parameter estimation approaches

for signal monitoring and channel context detection in

practice, the second experiment was conducted. In this

experiment, a static antenna named ‘reference’ was placed

on the top of a wooden house while another antenna

named ‘rover’ was held by a pedestrian outdoors. The

‘rover’ first remained stationary for about 60 seconds.

Then it was moved into the first floor of the house, down

to the basement, back outdoors for a while, and then

finally back to the first floor of the house. Both outdoor

and indoor signals were processed simultaneously by a

modified version of the GSNRxTM

software receiver

called GSNRx-rrTM

. The processing architecture of

GSNRx-rrTM

is shown below. Basically it processes

outdoor signals by a standard tracking loop to aid the

indoor channels via navigation data bits, carrier Doppler,

code phase, and carrier phase. Open-loop tracking is used

for indoor signal processing. In short, it is an AGPS type

of processing. For more details on this software, please

refer to Satyanarayana et al. (2010).


Figure 16 Processing architecture of GSNRx-rrTM

The data was processed with 100 ms, 500 ms, and 1 s of

coherent integration to investigate the impact of coherent

integration on signal parameter estimation and multipath

separation in frequency domain. The estimated rover

relative carrier Doppler and code phase with respect to the

reference estimates are plotted below. Clearly, the results

with 100 ms of coherent integration not only are noisy but

also fluctuate a lot in the local search domain compared to

the estimates from 500 ms and 1 s of coherent integration.

Figure 17 Rover relative carrier Doppler on PRN 22

Figure 18 Rover relative code phase on PRN 22

Figure 19 Rover relative carrier Doppler on PRN 11

Figure 20 Rover relative code phase on PRN 11

The estimated trajectories based on three different

coherent integrate times are show in Figure 21. A

standard Kalman filter was used in all three cases. The red

line, the green line, and the blue line represent the

estimated trajectory with coherent integration times 100

ms, 500 ms, and 1 s respectively. It can be observed that

some parts of the estimated trajectory (dots highline in

yellow) from 100 ms of coherent integration are totally

off from the building. Based on the time history, these

points should be within the area of the basement. After

examining the relative code phase and carrier Doppler

estimates associated with these points, it can be concluded

that the errors are due to the lack of sensitivity and

multipath errors. The estimated trajectories with 500 ms

and 1 s coherent integration times are reasonably

consistent. The epochs associated with the change of the

channel context (i.e. outdoor to indoor, indoor to outdoor)

were extracted from the trajectory with 1 s coherent

integration, since it should be the best one among all

three.


Figure 21 Estimated trajectories

As mentioned previously, the chip shape monitoring

approach may not be suitable in high signal degradation

environments. Chip shape was generated by 2 s of signal

compression based on standard closed-loop tracking. As

shown in Figure 22, the signal amplitude variation due to

the change of channel (i.e. outdoor to indoor) or loss of

lock of the close loop tracking can be easily observed,

however the chip shape distortion is hard to observe as

expected.

Figure 22 Chip shape from outdoor to indoor

The estimated C/N0 and K-factor values based on

envelope-based estimators with various coherent

integration times and moving window sizes are shown

below. Comparing the C/N0 and K-factor values on PRN

22, the mean value of C/N0 is approximately 40 dB-Hz

after the antenna moved indoors to the first floor, but the

C/N0 value varies from 53 dB-Hz to 28 dB-Hz in the case

of 100 ms coherent integration time. If the signal quality

or channel quality was indicated by the instantaneous

C/N0 value only, an optimistic decision might have been

made. In some literature, channel context such as indoor

or outdoor was detected or determined based on the

instantaneous C/N0 value only (Skournetou and Lohan

2007). Apparently this is too optimistic as shown in

Figure 23 and Figure 25. In order to monitor the

signal/channel and detect the channel context, the

indicator must be able to reflect the fading level over a

short duration of time. As shown in these figures, the K-

factor, which is a fading level indicator, performs very

well on signal/channel monitoring and detecting the

transition moving between outdoors and indoors.

Comparing the estimates from three envelope-based K-

factor estimators, the results from the two with 1st and 2

nd

moments are almost identical while the estimates from the

one with 2nd

and 4th

moments are much noisier and more

‘faded’ especially in the low C/N0 range. This is because

the noise is amplified more significantly in the 4th

moment estimation compared to the 1st moment

estimation. The performance difference between these

estimators can be reduced by utilizing a longer coherent

integration as shown in Figure 26.

Figure 23 C/N0 on PRN 22 with coherent integration

time 100 ms

Figure 24 K-factor on PRN 22 with coherent

integration time 100 ms



time 1 s


integration 1 s


time 500 ms


integration time 500 ms

In this experiment, one interesting observation was the

multipath signal separation in frequency domain. After a

long coherent integration, the frequency resolution is very

high; therefore, the direct signal and specular multipath

components can be separated. This phenomenon is

illustrated from Figure 29 to Figure 31. Therefore, in a

vector-based tracking loop operating in a kinematic mode,

the LOS signals can be separated from NLOS signals by

comparing the Doppler values of all peaks with the

estimated Doppler value predicted from the navigation

solution after a long enough coherent integration.

Figure 29 Correlation of 1 s coherent integration




CONCLUSIONS

In this paper, signal compression and fading parameter

(e.g. Ricean K-factor) monitoring are applied for GNSS

signal monitoring and channel context detection. Three

categories of Ricean K-factor estimators, namely

envelope-based, envelope/phase-based and phase-based,

are introduced. Their performance, limitations, and

practical implementation challenges on a high sensitivity

GNSS receiver are investigated. Based on the experiments

conducted during this research, signal compression can

only be applied in relatively light fading environments,

while the Ricean K-factor demonstrates excellent

performance on channel context detection and signal

monitoring both indoor and outdoor. The Ricean K-factor

can be used with C/N0 together to improve the robustness

of vector-based tracking loops.

ACKNOWLEDGMENTS

The assistance of Dr Daniele Borio in the development of

the software, and Shashank Satyanarayana, Pratibha

Anantharamu and Mohammed Tamazin for the data

collection is greatly appreciated.

This work was conducted with funding provided by

iCORE, part of Alberta Innovates – Technology Future.

REFERENCES

Azemi, G., B. Senadji, and B. Boashash (2003)

Estimating the Ricean K-factor for Mobile

Communication Applications, Proceedings of Signal

Processing and Its Applications, 2003.

Baddour, K.E. and T.J. Willink (2007) Improved

Estimation of the Ricean K Factor from I/Q Samples,

Vehicular Technology Conference, Fall, 2007.

Chen, Y. and N. C. Beaulieu (2005) Maximum

Likelihood Estimation of the K Factor in Ricean Fading

Channels, IEEE Communications Letters, Vol 9. No. 12.

December 2005.

Fenton, P. and J. Jones (2005) The Theory and

Performance of NovAtel Inc.’s Vision Correlator,

Proceedings of GNSS 2005 (Long Beach, CA), The

Institute of Navigation.

Kazemi, P. (2008) Optimum Digital Filters for GNSS

Tracking Loops, Proceedings of GNSS08 (Savannah, GA,

16-19 Sep, Session C5), The Institute of Navigation.

Lashley, M. and D. M. Bevly (2009) Vector

Delay/Frequency Lock Loop Implementation and

Analysis, Proceedings of ION NTM 2009.

Nielsen, J., S. Shanmugam, M. U. Mahfuz, G. Lachapelle

(2009) Enhanced Detection of Weak Signals Using

Spatial Combinging, Navigation, Journal of The Institute

of Navigation, Vol. 56, No. 2, pp 83-95.

O’Driscoll, C., D. Borio, M.G. Petovello, T. Williams and

G. Lachapelle (2009) The Soft Approach: A Recipe for a

Multi-System, Multi-Frequency GNSS Receiver, Inside

GNSS Magazine, Volume 4, Number 5, pp. 46-51.

(http://www.insidegnss.com/node/1635)

Pany, T., J. Winkey, B. Riedl, M. Restle, T. Worz, R.

Schweikert, H. Niedermeier, G. Ameres, B. Eissfeller, S.

Lagraster and G. Lopez-Risueno (2009) Performance of a

Partially Coherent Ultra-Tightly Coupled GNSS.INS

Pedestrain Navigation System Enabling Coherent

Integration Times of Several Seconds to Track GNSS

Signals Down to 1.5 dBHz, Proceedings of GNSS09

(Savannah, GA, 22-25 Sep), The Institute of Navigation,

16 pp.


Pany, T. and B. Eissfeller (2006) Use of a Vector Delay

Lock Loop Receiver for GNSS Signal Power Analysis in

Bad Signal Conditions, in ION Annual Meeting/IEEE

PLANS, San Diego, CA.

Petovello, M.G., C. O'Driscoll and G. Lachapelle (2008)

Weak Signal Carrier Tracking Using Extended Coherent

Integration with an Ultra-Tight GNSS/IMU Receiver,

Proceedings of European Navigation Conference

(Toulouse, 23-25 April), 11 pages.

Petovello, M.G. and G. Lachapelle (2006) Comparison of

Vector-Based Software Receiver Implementations With

Application to Ultra-Tight GPS/INS Integration.

Proceedings of GNSS06 (Forth Worth, 26-29 Sep,

Session C4), The Institute of Navigation, 10 pages.

Satyanarayana, S., D. Borio and G. Lachapelle (2010)

Power Levels and Second Order Statistics for Indoor

Fading Using a Calibrated A-GPS Software Receiver.

Proceedings of GNSS10 (Portland, OR, 21-24 Sep,

Session E1), The Institute of Navigation.

Schmid, A., C. Gunther, and A. Neubauer (2005) Rice

Factor Estimation for GNSS Reception Sensitivity

Improvement in Multipath Fading Enviroments,

Proceedings of the 2nd

Workship on Positioning,

Navigation and Communication, 2005.

Skournetou, D., and E. S. Lohan, Indoor location

awareness based on the non-coherent correlation function

for GNSS signals, In Proc. Of Finnish Signal Processing

Symposium (FINSIG 07), Oulu, Finland, August 2007

Tepedelenlioglu, C., A. Abdi, and G. B. Giannkis (2003)

The Ricean K Factor: Estimation and Performance

Analysis, IEEE Transactions on Wireless

Communications, Vol. 2, No. 4, July 2003.

Weil, L. (2007) Theory and Applications of Signal

Compression in GNSS Receivers, in Proceedings of ION

GNSS ITM07.

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