Kernel Methods for Accurate UWB-Based Ranging with Reduced Complexity

Kernel Methods for Accurate UWB-Based Ranging with Reduced Complexity
Kernel Methods for Accurate UWB-Based
Ranging with Reduced Complexity
Vladimir Savic, Erik G. Larsson, Javier Ferrer-Coll and Peter Stenumgaard
Linköping University Post Print
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Vladimir Savic, Erik G. Larsson, Javier Ferrer-Coll and Peter Stenumgaard, Kernel Methods
for Accurate UWB-Based Ranging with Reduced Complexity, 2015, IEEE Transactions on
Wireless Communications, 99, 1.
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Postprint available at: Linköping University Electronic Press
Kernel Methods for Accurate UWB-Based Ranging
with Reduced Complexity
Vladimir Savic, Erik G. Larsson, Javier Ferrer-Coll, and Peter Stenumgaard
Abstract—Accurate and robust positioning in multipath environments can enable many applications, such as search-andrescue and asset tracking. For this problem, ultra-wideband
(UWB) technology can provide the most accurate range estimates,
which are required for range-based positioning. However, UWB
still faces a problem with non-line-of-sight (NLOS) measurements, in which the range estimates based on time-of-arrival
(TOA) will typically be positively biased. There are many
techniques that address this problem, mainly based on NLOS
identification and NLOS error mitigation algorithms. However,
these techniques do not exploit all available information in the
UWB channel impulse response. Kernel-based machine learning
methods, such as Gaussian Process Regression (GPR), are able
to make use of all information, but they may be too complex
in their original form. In this paper, we propose novel ranging
methods based on kernel principal component analysis (kPCA),
in which the selected channel parameters are projected onto a
nonlinear orthogonal high-dimensional space, and a subset of
these projections is then used as an input for ranging. We evaluate
the proposed methods using real UWB measurements obtained in
a basement tunnel, and found that one of the proposed methods is
able to outperform state-of-the-art, even if little training samples
are available.
Index Terms—ranging, positioning, ultra-wideband, time-ofarrival, kernel principal component analysis, Gaussian process
regression, machine learning.
UWB [2] is a promising technology for range-based positioning in multipath environments. The large bandwidth of
a UWB signal provides a high temporal resolution for TOAbased ranging, and allows propagation through thin obstacles.
However, it still faces a problem with NLOS measurements
caused by thick obstacles (i.e., when the direct path is completely blocked), in which case TOA-based range estimates
will typically be positively biased. This is especially a problem
in environments such as tunnels, warehouses, factories and
urban canyons. Consequently, a positioning system based on
UWB range estimates may provide poor performance unless
careful action is taken. There are many techniques (see Section II) that address this problem. These techniques are mainly
based on NLOS identification and NLOS error mitigation
algorithms, but they do not exploit all available information
V. Savic and E. G. Larsson are with the Dept. of Electrical Engineering (ISY), Linköping University, Sweden (e-mails: [email protected],
[email protected]). Javier Ferrer-Coll is with the Dept. of Electronics,
Mathematics and Natural Sciences, University of Gävle, Sweden (e-mail:
[email protected]). Peter Stenumgaard is with the Swedish Defense
Research Agency (FOI) (e-mail: [email protected]).
This work was supported by the project Cooperative Localization
(CoopLoc) funded by the Swedish Foundation for Strategic Research (SSF),
and Security Link. A part of this work was presented at IEEE SPAWC
workshop, June 2014 [1].
in the UWB channel impulse response. Kernel-based machine
learning methods, such as GPR [3], are able to make use of
all information, but they may be too computationally complex
in their original form.
In this paper, we propose1 novel ranging methods based on
a nonlinear version of principal component analysis (PCA),
known as kernel PCA (kPCA) [4], in which the selected
channel parameters are projected onto a nonlinear orthogonal
high-dimensional space, and a subset of these projections
is then used as an input for ranging. Although popular for
computer vision problems, to the best of our knowledge, this
technique has not so far been used for UWB-based ranging.
We evaluate the proposed methods using real UWB measurements obtained in a basement tunnel of Linköping university.
The most important conclusion is that one of the proposed
methods, referred to as kPCA+GPR (which makes combined
use of kPCA and GPR techniques), is able to outperform stateof-the-art. Moreover, kPCA+GPR is also much faster than
direct GPR-based ranging.
The remainder of this paper is organized as follows. In
Section II, we provide an overview of state-of-the-art UWBbased ranging techniques. In Section III, we review the generic
approach for TOA-based ranging with NLOS identification
and error mitigation. Then, in Section IV, we overview a
GPR technique for UWB-based ranging, and propose novel
ranging methods based on kPCA. Our experimental results,
using UWB measurements obtained in a tunnel environment,
are provided in Section V. Finally, Section VI summarizes our
conclusions and provides some proposals for future work.
TOA estimates obtained from measured UWB impulse
responses are typically biased in NLOS scenario. There are
many proposals in the literature for solving this problem [5].
These techniques i) first attempt to distinguish between LOS
and NLOS conditions, and ii) then, if a NLOS condition is
detected, they mitigate the NLOS error. In what follows, we
survey the available algorithms for both these sub-problems.
1) NLOS identification. This problem could be carried
out by analyzing the variability between consecutive range
estimates [6], [7], relying on the assumption that NLOS
measurements typically have much larger variance than LOS
measurements. However, this approach would lead to very
high latency since it requires a large number of measurements. An alternative approach is to extract multiple channel
1 This paper is a comprehensive extension of our conference paper [1]. New
material includes: a detailed description of GPR for ranging, two new hybrid
methods (kPCA+ and kPCA+GPR), and many additional experiments.
parameters from the channel impulse response, and make a
NLOS identification based on these parameters. For example,
in [8] three parameters are used (RMS delay-spread, TOA and
received signal strength (RSS)). This study found that RMS
delay spread is the most useful parameter for this problem,
but a combination of all three parameters can reduce the
misclassification rate. Another study [9] found that the kurtosis
provides valuable information for the NLOS identification.
In [10], a non-parametric least-square support-vector-machine
(LS-SVM) classifier is used. This approach does not require
any statistical model, but directly works with the training
samples. A non-parametric approach is also used in [11],
to construct the probability density functions (PDFs) of the
LOS and NLOS ranging errors from training samples. Then,
Kullback-Leibler divergence is used to quantify the distance
between these PDFs, and set the decision threshold.
2) NLOS error mitigation. Once NLOS identification is
performed, one possibility is to simply discard all NLOS
measurements, but this would lead to unnecessary loss of
information. Therefore, NLOS error mitigation is required to
make NLOS measurements useful for ranging. Since the distribution of the NLOS error depends on the spatial distribution of
the scatterers, the mitigation could be performed by modeling
these scatterers [12], [13]. However, this approach is typically
infeasible due to the complex geometry of the environment,
and the possible presence of dynamic obstacles. Another way
is to model the NLOS error as a function of some channel
parameter. For example, in [14], the authors found that the
NLOS error increases with the mean excess delay and the
RMS delay spread. Therefore, a simple empirical model can
be used to significantly reduce this error.
In many cases, it may not be possible to detect an NLOS
condition with full certainty. In that case, a soft decision can be
taken, that is, the probability of NLOS condition is estimated.
NLOS identification and error mitigation are then combined
into one single step [15], and the ranging likelihood function
becomes a mixture of LOS and NLOS models. This kind of
model is also obtained in [16] by analyzing the measurements
from a tunnel environment. Finally, non-parametric kernelbased regression can be also used to estimate the NLOS
error as a function of multiple channel parameters, without
explicit NLOS identification. For this purpose, GPR, LS-SVM
regression, and relevance vector machines were used in [3],
[10], and [17], respectively. These methods can achieve much
better ranging performance than standard TOA-based methods,
but their complexity is much higher.
Our goal is to estimate the range using the received UWB
signal. We first make the following assumptions:
• We use a single UWB measurement for ranging.
• There are enough training UWB measurements (obtained
offline or online, depending on the environment) for
parameter estimation and learning.
• The precise floorplan of the area (especially, the positions
of the scatterers and irregularities) is not available, but
there exist surfaces that cause multipath propagation.
If the first path in the impulse response is undetectable,
the measurement is NLOS. Otherwise, even if the first
path is not dominant [18], the measurement is LOS. This
definition is reasonable because only an undetectable first
path could lead to a serious ranging error [16].
• The probability of NLOS condition is larger than zero
(otherwise, the ranging would be trivial).
Then, taking that we have measured
P the complex impulse
responses of the channel2 , h(t) = k ak δ(t − τk ) (ak ∈ C,
k = 1, . . . , Nk , Nk > 1, and τk is the delay of the k-th path),
we obtain its squared amplitude, |h(t)| , known as power
delay profile (PDP). However, since most of the components
of the PDP are typically caused by thermal noise, we consider
only the components above a certain threshold pT H [dBm],
|h(t)| , if |h(t)| > pT H
ph (t) =
The threshold pT H is usually chosen empirically [16], [19],
[20] so as to satisfy desired criteria (e.g, minimize the falsealarm and the missed-detection rates). Then, we can extract a
number of channel parameters from the PDP, such as TOA,
RSS, RMS delay spread [16]. From these parameters, the
range can be determined. A naive way of computing the range
estimate would be to take dˆT OA = cτ1 where τ1 is estimated
TOA, and c ≈ 3 · 108 m/s is the speed of light. However,
this would typically lead to a large positive error in the NLOS
scenario. Thus, NLOS identification and error mitigation are
required to reduce this error.
In what follows we describe a general approach for estimating the range from the channel parameters, which is essentially
the basis for the methods in [15], [16]. We define the binary
variable H ∈ {LOS, NLOS}, and consider the following
d + µL + νL ,
if H = LOS
cτ1 =
d + g(αE ) + νN ,
if H = NLOS
where d is the true distance between transmitter and receiver,
νL and νN are LOS and NLOS noise components (νL ∼ pL (·),
νN ∼ pN (·)), respectively, and µL is a known LOS bias
(caused by finite bandwidth, false alarms, or systematic errors).
g(αE ) is the NLOS error modeled as a (potentially nonlinear)
function of some appropriately selected channel parameter αE
(see Section V-B). αE should be empirically chosen so that it
is strongly correlated with the NLOS error. In order to make
a soft decision on H, we use Bayes’ rule:
p(αI |H)p(H)
H 0 ∈{LOS,NLOS} p(αI |H )p(H )
p(H|αI ) = P
where p(H) is the prior, and p(αI |H) is the likelihood
function, and αI is another channel parameter. αI should
be empirically chosen to be a good discriminator between
LOS and NLOS. The prior on H may be chosen based on
knowledge of the geometry of the area, or assumed noninformative. The model for likelihood p(αI |H) in (3) depends
on the chosen channel parameter αI and can be obtained using
2 Otherwise, if the output waveforms are available, a multipath extraction
technique (such as CLEAN or ESPRIT) would be required.
training measurements.
The likelihood function for range estimation has a mixture
p(τ1 , αI , αE |d) ∝ p(H = LOS|αI )pL (cτ1 − µL − d) +
p(H = NLOS|αI )pN (cτ1 − g(αE ) − d) (4)
This (non-Gaussian) likelihood represents full statistical information about unknown distance (assuming a non-informative
prior), and can be used for Bayesian positioning algorithms.
Assuming that pL (·) and pN (·) are zero-mean Gaussian
(with variances σL
and σN
), the minimum mean-squareerror (MMSE) estimate of the distance, and the corresponding
variance, are given by:
dˆT OA,M = p(H = LOS|αI ) (cτ1 − µL ) +
p(H = NLOS|αI ) (cτ1 − g(αE ))
σT2 OA,M =
p(H = LOS|αI ) (cτ1 − µL − dˆT OA,M ) + σL
p(H = NLOS|αI ) (cτ1 − g(αE ) − dˆT OA,M ) + σ 2 (6)
There are many variations of this approach available in
literature. A common (but sub-optimal) approach [8], [21],
[22] is to make a hard decision on LOS/NLOS, and then
estimate the distance. It is also possible to use multiple channel
parameters for this problem [8], but this would likely lead
to over-counting of the same information since the channel
parameters are typically correlated [16]. 3 The optimal solution
for this problem would require a properly estimated joint
distribution of the parameters which is a challenging problem
in the presence of many parameters. Another way to solve this
problem is by using kernel methods as will be shown in the
following section.
The goal is to perform ranging using all available channel
parameters from the PDP given by (1). Since some of the
kernel methods operate with centered and dimensionless data,
we first transform the channel parameters αk (k = 1, . . . , K)
as follows:
αk − µαk
ak =
where µαk and σαk are the mean and the standard deviation
of αk , respectively. Then, we gather all transformed channel
parameters into one vector a = (a1 , . . . , aK )T , that will be
used as an input for the kernel methods. In the following
subsections, we will first describe a state-of-the-art method
based on GPR (given in [3]), and then three novel methods
based on kPCA.
A. Gaussian process regression (GPR)
In this subsection, we describe state-of-the-art in using GPR
for range estimation, which will serve as a baseline in the
comparisons. We consider the following regression problem:
3 Over-counting is a problem that occurs when multiple correlated random
variables are assumed to be uncorrelated. This problem typically leads to
under-estimated variance.
d = f (a) + ω
where f (·) is a nonlinear function of the channel parameters,
and ω is a Gaussian random variable (ω ∼ N (0, σω2 )) that
represents the error of fitting. The problem can be solved by
defining f (·) in a parametric form, and learning its parameters.
However, it is often infeasible to decide which function is
appropriate, so we assume that this function is random, and
follows a Gaussian process (GP) [23], [24]: f (·) ∼ GP(m, K)
where m is a mean function and K is a covariance function
(also known as a kernel matrix). Note that it does not mean that
the underlying process is precisely Gaussian, but we can still
use GP as a maximum entropy process, for a given covariance
function. For the problem at hand, we do not know the mean
function, so it is reasonable to assume4 that m = 0. The kernel
matrix K is used to model the correlation between output
samples as a function of the input samples.
Assuming that we have available a set of i.i.d. training
samples TN = {dn , an }N
n=1 and a single measurement a,
we would like to determine the posterior density of the
distance, i.e., p(d|a, TN ). We also define the following sets:
fN = (f (a1 ), . . . , f (aN )), AN = (a1 , . . . , aN ) and dN =
(d1 , . . . , dN ). Taking into account the previous assumptions
and definitions, the likelihood function and the GP prior over
the training samples are, respectively, given by:
p(dN |fN ) =
p(dn |f (an )) =
N (dn ; f (an ), σω2 ) (9)
p(fN |AN ) = N (fN ; 0, K)
where (K)ij = k(ai , aj ) (∀ai , aj ∈ TN ), k(·) is a kernel
function, and N (x; µx , Σx ) stands for a Gaussian distribution
of the random variable x with mean µx and covariance matrix
Σx . A widely used kernel function is a weighted sum of the
squared exponential and linear terms, given by:
k(ai , aj ) = θ0 e−θ1 kai −aj k + θ2 aTi aj
where the hyperparameters θ = (θ0 , θ1 , θ2 ), along with σω ,
can be estimated from the training data by maximizing the
log-marginal likelihood log p(dN |AN , θ, σω ). This can be
achieved numerically using some gradient-based optimization
technique (more details in [23]). With this kernel, the correlation between the output samples is higher if the Euclidean
distance between the corresponding input samples is smaller.
Note that the standard linear regression (f (a) = wT a) is
obtained as a special case by setting θ = (0, 0, 1).
Now, we extend the training sample set with a test measurement a, and define the extended GP prior:
p(f (a), fN |a, AN ) = N
; 0,
f (a)
kT k(a, a)
where k = [k(a1 , a), k(a2 , a), . . . , k(aN , a)] . To include
4 More precisely, if we do not know the mean function, we can assume a
prior over it, and integrate it out. The resulting process will be zero-mean if
this prior is symmetric around zero. For our problem, we do not have any
information about the mean function, so we can assume a noninformative
prior and set p(m) = const.
the information from the likelihood, we apply Bayes’ rule:
p(f (a), fN |a, TN ) ∝ p(dN |fN )p(f (a), fN |a, AN )
principal component
in linear sub-space
where we used the fact (see eq. (8)) that dN is independent
of {a, AN }, given fN . Finally, we find the desired posterior
by marginalizing over the latent function:
p(d|a, TN ) =
p(d|f (a))p(f (a), fN |a, TN )dfN df (a)
where p(d|f (a)) = N (d; f (a), σω2 ), and
denotes N + 1
integrations. Since all distribution inside the integral are
Gaussian, the posterior
is also Gaussian, so p(d|a, TN ) =
N d; µGP R , σGP
R with the parameters given by:
principal component in
nonlinear sub-space
µGP R = kT (K + σω2 IN )−1 dN
R = σω + k(a, a) − k (K + σω IN )
The MMSE estimate of the distance is given by dˆGP R =
µGP R and the remaining uncertainty by the variance σGP
Therefore, GPR is capable of providing full statistical information (i.e., both the mean and the variance). This stands in
contrast to other machine learning methods such as SVM.5
Another important characteristic of GPR is that σGP R is small
in the areas where the training samples lie, and large in the
areas with no (or few) training samples. Finally, assuming
offline training, we can precompute the inverse of the matrix
K + σω2 IN and store into memory,6 so the complexity of GPR
is O(N 2 ).
Note that there is a minor difference between the original
method in [3] and the algorithm above. In [3], GPR is used to
estimate the bias of the TOA-based range estimate, whereas
the above method directly estimates the range.
B. Kernel Principal Component Analysis (kPCA)
The GPR method, described in Section IV-A, provides the
optimal estimate [24] of the distance in the MMSE sense under
the assumption that the unknown function f (·) follows a GP.
However, in some applications it may be too computationally
complex, especially, if there are many training samples. In this
section, we describe an alternative approach based on kPCA,
which is proposed in a preliminary form in [1].
Instead of directly using the raw channel parameters a, one
possible approach could be to apply eigenvalue decomposition
to decorrelate them, and then retain M largest eigenvalues
and corresponding eigenvectors, that can be used for ranging.
This procedure is known as principal component analysis
(PCA). However, PCA projects the data in a linear sub-space,
so it is not useful if the data lies on a nonlinear manifold.
An example for 2D data is shown in Fig. 1. Therefore, we
propose to apply kernel PCA (kPCA) initially developed in
its general form in [4]. kPCA differs from PCA in that it
projects the data to an arbitrary nonlinear manifold. kPCA
5 A variation of SVM can provide a measure of uncertainty, but in an adhoc way, as pointed out in [23, Section 6.4]. GPR provides the best possible
estimate in the Bayesian sense (by finding the posterior using Bayes rule).
6 This computation requires O(N 3 ) operations, but it is done only once in
this case.
Fig. 1: Principal components of 2D data in (a) linear and, (b) nonlinear subspace. The latter one is much more accurate projection.
is already successfully applied to pattern recognition from
images [4], and it is found that nonlinear principal components
can provide much better recognition rates than corresponding
linear principal components.
Consider a nonlinear transformation φ(a), which transforms
K-dimensional vector a to an N -dimensional vector in a
feature space. The feature space has large (possibly, infinite)
dimension (N >> K). For now, we also assume that φ(a)
has zero mean, so that its N × N covariance matrix is given
by C = E(φ(a)φ(a)T ). Then, we could find the principal
components using eigenvalue expansion of C, but this is not
possible to compute explicitly since the feature space has high
dimension. However, it can be shown [25, Chapter 12] that
eigenvalue decomposition depends on φ(a) only via the inner
product k(a, an ) = φ(a)T φ(an ) (where an is a training
sample, i.e., an ∈ T ), known as a kernel function. Thus,
we find principal components using the following eigenvalue
Kvn = λn N vn
where K is an N × N kernel matrix7 with (K)ij = k(ai , aj ),
∀ai , aj ∈ T . The eigenvalues and eigenvectors of K are
given by λn N and vn (n = 1, . . . , N ), respectively. Then,
the projection of a test point a onto eigenvector i is given by
yi (a) =
vin k(a, an )
Therefore, we can obtain all projections using kernel functions,
without explicit work in feature space. In practice, the feature
vector is not zero-mean, but it only means that we need to
replace K with a Gram matrix K̃:
K̃ = K − 1N K − K1N + 1N K1N
where 1N is the N × N matrix with all values equal to 1/N .
We see that this matrix can be also computed without the
feature vectors. Therefore, for kPCA, we only need to define a
kernel function. Popular choices [4] are Gaussian, sigmoid and
polynomial kernels. Since we know that the range is a linear
function of the TOA in case of LOS, and possibly a nonlinear
function of all features in case of NLOS, a kernel that can
model both linear and nonlinear relationships is appropriate.
7 Although we use the same symbol for the kernel matrix in GPR and kPCA,
they represent the different quantities.
Therefore, we choose a polynomial kernel, given by:
k(a, an ) = (a an + 1)
C. Hybrid methods: kPCA+ and kPCA+GPR
where c ∈ N is the degree of the polynomial. The degree
c can be found empirically. Note that the standard PCA
is a special case that results if we select the linear kernel
k(a, an ) = aT an .
Then, given M principal components yi (a) corresponding
to the M largest eigenvalues, we need to provide a model
that will relate them with the unknown range. Since this
relationship is unknown in general, we use a simple linear
model. A nonlinear model would be redundant anyway since
we already had the opportunity to perform arbitrary nonlinear
transformations using kPCA. Therefore, we assume that the
projected features yi = yi (a) are linear functions of the true
yi = b1,i d + b0,i + νy,i
where b1,i and b0,i are parameters that can be found using
least-squares curve fitting (using the same training samples,
T ), and νy,i is a noise component. Since the distribution
of the noise is also unknown, we assume that it is zeromean Gaussian as the distribution that maximizes the entropy
(given the variance σy,i
, which can be found from the training
samples). Assuming that we retain M principal components,
and taking into account that they are orthogonal to each other,
the likelihood function is given by:
p(y1 , . . . , yM |d) =
p(yi |d) =
The maximum number of principal components is equal to
the total number of the training samples (N ), which is much
higher than the dimensionality of the original data (K). Since
M is expected to be larger than K, the main purpose of kPCA
is nonlinear feature extraction, in contrast to PCA, which is
typically used for dimensionality reduction.
From (22), we can find the MMSE estimate of the distance,
yi − b0,i
dkP CA = σkP CA
CA =
i=1 y,i
p(y1 , . . . , yM 0 |H) =
p(yi |H)
where M 0 is the number of retained features for this classification problem (not necessarily equal to M ). Then, the posterior
distribution of H is given by
N (yi ; b1,i d + b0,i , σy,i
y −b
N d; ib1,i0,i , b2y,i
where the parameters µL,yi , σL,yi , µN,yi , σN,yi are found from
the training set. This approximation will cause some loss of
information, but we are now able to use multiple uncorrelated
features, in contrast to the approach described in Section III.
Consequently, the total likelihood function is given by:
One problem of the kPCA and GPR methods described
above is that they do not exploit the fact that some measurements will be LOS, and in that case the range can be directly
obtained from the TOA. Hence, the kernel methods are only
needed to handle NLOS measurements. However, since it may
not be possible to make a reliable LOS/NLOS identification
(i.e., estimate H), we need to use a soft-decision approach in
a similar way as in Section III.
Since kPCA provides us many (up to N ) projected features
yi , it is logical to assume that some of them will provide us
useful information about H. However, the projected features
represent a complex and unknown function of the raw channel
parameters, so it is not easy to determine the precise model for
the distribution p(yi |H). Therefore, using the same principle
as in Section IV-B, we approximate this distribution with a
Gaussian as follows:
N (yi ; µL,yi , σL,y
), if H = LOS
p(yi |H) =
N (yi ; µN,yi , σN,y
), if H = NLOS
is the variance of the estimate. Recall that this estimate is valid
for both LOS and NLOS scenarios, so NLOS identification is
not needed. Assuming that eigenvalue decomposition is done
offline, the complexity of kPCA is O(M N ) since we need
to find only M principal components using (18). Therefore,
kPCA is approximately N/M times faster than GPR. However,
this approach will provide a sub-optimal result since the noise
in (21) is not Gaussian in the general case.
p(H|y1 , . . . , yM 0 ) ∝ p(y1 , . . . , yM 0 |H)p(H)
where p(H) is the prior distribution of H. Finally, the MMSE
estimate of the distance is given by:
dˆkP CA+ = p(H = LOS|y1 , . . . , yM 0 ) (cτ1 − µL ) +
p(H = NLOS|y1 , . . . , yM 0 )dˆkP CA
where dˆkP CA is the kPCA estimate of distance found by
(23), but with one difference: the training set consists only
of NLOS samples (the number of these samples is denoted
by NN ). The variance of this estimate can be computed
in the same fashion as the one in (6). Assuming that both
eigenvalue decompositions are done offline, the complexity of
this approach, referred to as kPCA+, is O(M 0 N + M NN ).
Alternatively, we may use GPR to estimate the distance
from the NLOS training samples. In that case, the distance
estimate is given by:
dˆkP CA+GP R = p(H = LOS|y1 , . . . , yM 0 ) (cτ1 − µL ) +
p(H = NLOS|y1 , . . . , yM 0 )dˆGP R
The complexity of this approach, referred to as kPCA+GPR,
is O(M 0 N +NN
). With a reasonable assumption that NN <<
N , kPCA+GPR would have a similar complexity as kPCA+,
and would be much faster than original GPR.
A possible problem of the proposed kernel algorithms
TABLE I: Measurement parameters
Signal power
Center frequency
Number of freq. points
Sweep time
Antenna range
Antenna gain
Cable attenuation
12 dBm
sinusoidal sweep
3.5 GHz
2 GHz
263 ms
0.5 ns (15 cm)
1.71 - 6.4 GHz
5 - 7.5 dBi
0.65 dB/m
is the training phase, in which we need to collect enough
measurements and corresponding ranges. This training can
be done either offline or online, depending how variable the
channel is in the considered environment. Since our goal is
ranging using UWB signals (which can pass through thin
obstacles [16]), the correct TOA can be estimated as long as
the first path is detectable (even attenuated). Therefore, if the
variability in the environment is caused by people walking
or other thin objects (the required level of thinness depends
on the signal bandwidth), the training can be done offline, as
in the environment considered in the next section. Otherwise,
e.g., in the presence of moving vehicles and other machinery,
the training should be done online. In that case, a set of predeployed anchors on known (manually recorded) positions
should be used. Their positions should be carefully chosen
(e.g., uniformly deployed) in order to provide a sufficient
statistics for training of parameters. This training can be
repeated periodically (with a lower frequency than the online
algorithm), or manually triggered once some change in the
environment is detected. Since this procedure may be too slow
in larger areas, the online training should be done only for
the parts of the area in which the environmental changes are
In this section, we analyze the performance of the proposed
ranging methods using UWB measurements obtained in a
basement tunnel of Linköping university (LiU) in Sweden.
Fig. 2: Illustration of LOS experiments in LiU tunnel.
Fourier Transform (IFFT), the complex impulse responses are
estimated, and then, PDPs are calculated. We summarize the
main parameters in Table I.
Using the described setup, the measurements have been
carried out in a basement tunnel of LiU (Fig. 2). The tunnel
walls, excluding the metal doors, are built of concrete blocks
with steel reinforcement. The ceiling is also made of concrete,
but with many metal pipes. Four different scenarios have
been considered: LOS, and NLOS caused by three different
obstacles: metal sheet, person, and tunnel wall. For each of
these scenarios, we placed the transmitter (Tx) in 3 positions
and receiver (Rx) in 30 positions forming the route through
the tunnel, as shown in Fig. 3. For each Tx-Rx pair, we
obtained 10 PDPs, so we obtained 3600 PDPs in total (900 per
scenario). Since our initial analysis showed that thin obstacles
(metal sheet and person) provide results similar as LOS (i.e.,
the direct path is detectable), we will consider them as LOS in
the analyses. Therefore, we have available 2700 LOS samples
and 900 NLOS samples. Half of these samples (1350 LOS
samples and 450 NLOS samples) will be used as training data,
and the rest will be used to test the ranging performance. Note
that the training samples should include all the scenarios that
are expected to be encountered in the online phase (i.e., LOS
and all types of NLOS).8
A. Measurement Setup and Scenarios
For our experiments, the measurement setup consisted of a
vector network analyzer (VNA), two UWB omni-directional
antennas and coaxial cables to connect the antennas with
the VNA. A personal computer is used to set the VNA
parameters and extract the multiple frequency responses from
the VNA. We used a swept-frequency sinusoidal signal (with
3001 points) to characterize the channel between 2.5 and 4.5
GHz. The power level was set to 12 dBm, and a calibration
of the system is performed to compensate for the effects of
VNA, cables and antennas. Then, the frequency responses
are transferred to the PC where a Hann window is used to
reduce the out-of-band noise, and to ensure causality of the
time-domain responses. Finally, by applying the Inverse Fast
B. Model Selection
We first extracted many parameters from the available
PDPs (i.e., all parameters that have some physical meaning).
Then, we tested the cross-correlation between each pair of
parameters, and removed those that do not provide any additional information. From the remaining subset, we removed
those which have negligible correlation with both the true
range and the NLOS bias. The remaining K = 8 channel
parameters are used for kernel methods: TOA, RSS, maximum
received power, mean excess delay, maximum excess delay,
8 We also tested other appropriate training/test splits, and found that they
provide nearly the same results.
Tx 6
Tx 1
2.9 m
Tx 2
Rx route (Rx 1 – Rx 30)
Tx 3
Tx 4
30 m
Tx 5
3.7 m
5.8 m
Fig. 3: Deployment of transmitters (Tx) and receivers (Rx) in the LiU tunnel. There are 6 transmitter positions (marked with green squares), and 30 receiver
positions (marked with red circles). Transmitters Tx 1-3 are used to test the LOS and two NLOS scenarios (by putting a metal sheet and a person in front of
them), while transmitters Tx 4-6 are used to test NLOS caused by tunnel wall. The height of the tunnel is 2.8 m.
TABLE II: The parameters estimated from the training samples.
Estimated values
0.16 m
1.61 m
0.333 ns−1
0.075 ns−1
(0.00087, −0.2, 11.72)
(64.6, 0.57, 1.59)
RMSE [m]
(p2 , p1 , p0 )
RMS delay spread, rise time and kurtosis (their definitions
are available in [16, Section IV]). This parameter set is
very similar to the sets [3], [10], [14], [17] widely used
for ranging in indoor environments. For TOA with NLOS
identification and error mitigation, we found that rise time
is the best channel parameter (αI ) for NLOS identification,
and maximum excess delay is the best channel parameter
(αE ) for NLOS error mitigation. Regarding the model in (2),
our measurements indicated that νL and νN approximately
follow a zero-mean Gaussian distribution (with variances σL
and σN ), while g(αE ) can be modeled with a second-order
polynomial function (i.e., g(αE ) = p2 αE 2 + p1 αE + p0 ).
The likelihood function p(αI |H) is assumed to follow an
exponential distribution with different decay rates in the LOS
and NLOS scenarios (given by λL and λN , respectively),
and the prior p(H) is assumed to be non-informative. The
justification of all these models is available in [16, Section
V]. For the baseline GPR method, we used the Matlab toolbox
(available online [26]) to obtain θ and σω , and to perform the
regression. The numerical values of all parameters are shown
in Table II. We note the following: i) the standard deviation
of the range estimate is much higher for the NLOS scenario
(σN >> σL ), ii) the LOS rise time decays much faster than the
NLOS rise time (λL >> λN ), and iii) the squared exponential
term in the kernel, given by (11), has much higher influence
than the linear term (θ0 >> θ2 ). Note that these parameters are
valid only for this specific environment, which means that the
training phase should be repeated for the other environments.
In order to empirically determine appropriate values of c
degree of polynomial kernel
Fig. 4: RMSE as a function of degree of polynomial kernel (c) for different
number of retained principal components (M ).
and M , we analyze root-mean-square error (RMSE) of the
kPCA range estimates as a function of degree c, for different
numbers of retained principal components M (see Fig. 4).
This metric is the most relevant for many applications, but
note that in some cases other metrics (such as median, or 95%
percentile) may be also useful. We see that it is necessary to
retain M ≥ 40 principal components. We choose M = 60
principal components, in which case the best performance is
achieved for c = 3. We also note that a wrong value of the
polynomial degree (e.g., c = 2) could lead to a significant loss
in performance. In general, too small values of c would lead
to under-fitting (i.e., the model is too simple), while too large
values would lead to over-fitting (i.e, the model is too adapted
to training samples).
Regarding NLOS identification based on kPCA (needed for
kPCA+ and kPCA+GPR), we analyze overlap metric [16],
which represents a measure of the overlap of two distributions considering only their means and variances (defined as
σL,yi σN,yi / |µL,yi − µN,yi | for i-th principal component),
and misclassification rate (defined as Nmiss /N , where Nmiss
rise time
overlap metric
TOA with mitigation
95th percentile
principal component
rise time
missclassification rate
error [m]
Fig. 6: CDF of the ranging error for all considered algorithms. The results are
based on 1800 test samples (of which 75% are LOS and 25% are NLOS).
number of retained principal components (M’)
Fig. 5: NLOS identification using kPCA and rise time: (a) overlap metric for
different principal components, and (b) misclassification rate as a function of
M 0.
is the number of misclassifications). As we can see in Fig.
5a, the overlap between LOS and NLOS distributions have an
oscillating behavior (as a function of i) and it is minimal for
i = 3. If we use only this principal component, we would not
be able to perform better than the NLOS identification based
on rise time. We also note an increasing tendency with i, since
the higher principal components include little information (i.e.,
have small eigenvalues). Therefore, it is reasonable to use the
first M 0 components as described in Section IV-C.9 In Fig.
5b, we can see that using kPCA for NLOS identification,
instead of the approach based on rise time, can significantly
reduce the misclassification rate with only M 0 ≥ 3 principal
components (we choose M 0 = 4 for further analyses). On the
other hand, the misclassification rate never reaches zero, which
means that a soft-decision ranging algorithm is still required.
The increasing tendency for M 0 > 6 is probably caused by
the fact that the uncorrelated principal components are not
necessarily independent.
C. Performance analysis
Given the previous models, our goal is to analyze the
ranging error of the proposed algorithms and compare it with
9 Using only the local minimums is not recommended since they may
change, especially in case of offline training.
TOA-based methods and GPR. Therefore, we analyze the
cumulative distribution function (CDF) of the ranging error,
shown in Fig. 6. We observe the following:
• The TOA-only method provides very accurate results for
about 77% of the samples (i.e., for all LOS and few
NLOS samples). However, NLOS samples can cause a
huge (up to 10 m) error. TOA with mitigation can reduce
this error by about 1 m, but it performs slightly worse
for LOS samples.
• The GPR method is the best approach for any percentile
above the 90th, but it performs worse than TOA-based
methods for the lower percentiles (i.e., mostly LOS
samples). That can be explained by the fact that the GPR
performs direct ranging using a combination of LOS and
NLOS samples (without NLOS identification).
• The kPCA method performs slightly worse (∼ 0.4 m)
than GPR for any percentile, due to the approximations
made in (21). However, this method is about N/M = 30
times faster than GPR. For the sake of completeness, we
also show the performance of kPCA with the linear kernel
(i.e., PCA), which provides a very poor performance, as
• Both kPCA+ and kPCA+GPR methods, thanks to the
NLOS identification, achieve the performance of the
TOA-only method for LOS samples. For NLOS samples,
they perform just slightly worse (∼ 0.5-1 m) than GPR.
Therefore, these two methods inherit the best characteristics of both state-of-the-art methods.
In summary, according to our experiments, kPCA+GPR
(which is slightly more accurate than kPCA+), is more appropriate method for ranging than other considered state-ofthe-art methods.
The previous analysis assumed that many training samples
are available. We also analyze the effect of the number of
training samples (N ) on ranging accuracy, for two representative methods: GPR and kPCA+GPR. The results are shown
in Fig. 7. As we can see, decreasing N leads to reduced per-
We proposed novel kernel methods for UWB-based ranging,
and tested them using real data from a tunnel environment.
All methods are much faster than the state-of-the-art kernel
method (GPR), since they use only a subset of orthogonal
principal components. Among the proposed methods, the
kPCA+GPR algorithm performs the best, and it outperforms
both GPR and two TOA-based methods. In addition, compared
to GPR, it requires fewer training samples. In summary,
the moderate complexity and high ranging performance of
kPCA+GPR makes this method useful for many critical applications, including emergency situations that require good
performance within a short time.
There remain many interesting lines for possible future
work. Since the kernel methods are trained to one particular,
fixed environment, an extension to multiple different environments (e.g., by using online training) could be of high
1800 samples
900 samples
450 samples
225 samples
112 samples
95th percentile
error [m]
formance of GPR, so we need at least 450 samples to keep its
performance reasonable. On the other hand, the performance
of kPCA+GPR is nearly constant for 225 ≤ N ≤ 1800.
The reason for somewhat remarkable behavior is twofold: i)
classification is a discrete problem, in contrast to regression, ii)
the kPCA+GPR estimates partly depend on TOA (see (29)),
which does not require any training samples. Therefore, an
additional advantage of kPCA+GPR is that it requires fewer
training samples, and consequently, it will be much faster.
Moreover, we can observe that, for 100 ≤ N ≤ 1500
training samples, kPCA+GPR performs the best, while for
N > 1500, GPR provides the best performance. However,
for very few training samples (N < 100), both algorithms
perform worse than TOA with mitigation (comparisons are
done at 95th percentile). Consequently, a hybrid algorithm
switching from one technique to another, depending on N ,
is also a reasonable option. In that case, the switching values
depend on the environment, and can be found empirically.
Finally, we analyze how different subsets of channel parameters affect the ranging performance. More specifically,
we compare GPR and kPCA+GPR ranging methods based
on i) all 8 channel parameters, ii) only TOA and RSS, and
iii) all channel parameters except TOA and RSS. As we
can observe in Fig. 8, it is beneficial to use more channel
parameters for ranging. However, the gain is small (about 0.5
m) due to the partial correlations between these parameters
(the correlation coefficients are available in [16, Table III]).
Another important observation is that, if ranging is performed
using all channel parameters except TOA and RSS, we can
get a similar performance (for higher percentiles) as with
ranging using TOA and RSS only. That means that ranging can
be performed even without traditional metrics, such as TOA
and RSS, which are used in many ranging and positioning
algorithms. Therefore, kernel-based machine learning methods
represent a very powerful toolbox since the same algorithm is
able to adapt to different input measurements, even if they are
related to the output in a complex nonlinear way.
We finish this section by summarizing the discussed characteristics of all considered methods in Table III.
1800 samples
900 samples
450 samples
225 samples
112 samples
95th percentile
error [m]
Fig. 7: CDF of the ranging error for different number of the training samples
(N ): (a) GPR, and (b) kPCA+GPR. The ratio between LOS and NLOS
samples is preserved in all considered cases.
interest. It would also be useful to study whether GPR could
be improved by replacing the Gaussian process with some
other tractable random process. Finally, finding a tractable
way to directly use channel impulse response measurements
(i.e., without selection of features) may provide us even better
ranging performance.
The authors would like to thank Per Ängskog and José Chilo
(University of Gävle) for their help during the measurement
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TABLE III: A summary of the main characteristics of the considered ranging methods.
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GPML Matlab Code.
Vladimir Savic received the Dipl.Ing. degree in
electrical engineering from the University of Belgrade, Belgrade, Serbia, in 2006, and the M.Sc. and
Ph.D. degrees in communications technologies and
systems from the Universidad Politecnica de Madrid,
Madrid, Spain, in 2009 and 2012, respectively. He
was a Digital IC Design Engineer with Elsys Eastern
Europe, Belgrade, from 2006 to 2008. From 2008
to 2012, he was a Research Assistant with the
Signal Processing Applications Group, Universidad
Politecnica de Madrid, Spain. He spent three months
as a Visiting Researcher at the Stony Brook University, NY, USA, and four
months at the Chalmers University of Technology, Gothenburg, Sweden. In
2012, he joined the Communication Systems (CommSys) Division, Linköping
University, Linköping, Sweden, as a Postdoctoral Researcher. He is co-author
of more than 25 research papers in the areas of statistical signal processing
and wireless communications. His research interests include localization and
tracking, wireless channel modeling, Bayesian inference, machine learning
and distributed and cooperative inference over wireless networks.
Erik G. Larsson received his Ph.D. degree from
Uppsala University, Sweden, in 2002. Since 2007,
he is Professor and Head of the Division for Communication Systems in the Department of Electrical
Engineering (ISY) at Linköping University (LiU)
in Linköping, Sweden. He has previously been Associate Professor (Docent) at the Royal Institute
of Technology (KTH) in Stockholm, Sweden, and
Assistant Professor at the University of Florida and
the George Washington University, USA. In the
spring of 2015, he was a Visiting Fellow at Princeton
University, USA, for four months.
His main professional interests are within the areas of wireless communications and signal processing. He has published some 100 journal papers
on these topics, he is co-author of the textbook Space-Time Block Coding
for Wireless Communications (Cambridge Univ. Press, 2003) and he holds 15
issued and many pending patents on wireless technology. He has served as
Associate Editor for several major journals, including the IEEE Transactions
on Communications (2010-2014) and IEEE Transactions on Signal Processing
(2006-2010). He serves as chair of the IEEE Signal Processing Society
SPCOM technical committee in 2015–2016 and as chair of the steering
committee for the IEEE Wireless Communications Letters in 2014–2015. He
is the General Chair of the Asilomar Conference on Signals, Systems and
Computers in 2015 (he was Technical Chair in 2012). He received the IEEE
Signal Processing Magazine Best Column Award twice, in 2012 and 2014, and
he is receiving the IEEE ComSoc Stephen O. Rice Prize in Communications
Theory in 2015.
Javier Ferrer-Coll received the B.Sc. degree in
telecommunication engineering in 2005 and the
M.Sc. in 2008 from the Universidad Politecnica
de Valencia, Spain. He received his Ph.D in 2014,
from the School of Information and Communication,
Royal Institute of Technology (KTH), Stockholm,
Sweden. From 2009 to 2014, he was employed at
the university of Gävle, Sweden. He is currently
employed as system engineer in the communication
department at Combitech AB. His research interest is
channel characterization in industrial environments,
particularly measurement system design to extract the channel characteristics.
Moreover, he is also interested in techniques to detect and suppress electromagnetic interferences.
Peter Stenumgaard has a Ph.D in radio communications from the Royal Institute of Technology
(KTH), Stockholm. He is currently a Research Director and works as Head of the Department of Information Security & IT architecture at the Swedish
Defence Research Agency (FOI) in Linköping, Sweden. He has worked as adjunct professor, both at
Linköping University, Sweden, and the University of
Gävle, Sweden. He has long experience of research
for both military and civilian wireless applications
and has also been the Director of the graduate
school Forum Securitatis (funded by Vinnova) within Security and Crisis
Management at Linköping University. He worked for several years on the JAS
fighter aircraft project with the protection of aircraft systems against electromagnetic interference, lightning, nuclear weapon-generated electromagnetic
pulse (EMP) and high-power microwaves (HPM). His research interests are
robust telecommunications for military-, security-, safety- and industrial use.
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