U.S. patent application number 11/653350 was filed with the patent office on 2007-05-24 for probabilistic model for a positioning technique.
This patent application is currently assigned to Ekahau Oy. Invention is credited to Pauli Misikangas, Petri Myllymaki.
Application Number | 20070117568 11/653350 |
Document ID | / |
Family ID | 8564054 |
Filed Date | 2007-05-24 |
United States Patent
Application |
20070117568 |
Kind Code |
A1 |
Misikangas; Pauli ; et
al. |
May 24, 2007 |
Probabilistic model for a positioning technique
Abstract
A model construction module for constructing a probabilistic
model of a wireless environment in which a target device
communicates using signals that have a measurable signal value,
such as signal strength. The model construction module forms
several submodels of the wireless environment. Each submodel
indicates a probability distribution for signal values at one or
more locations in the wireless environment. The module combines the
submodels to a probabilistic model of the wireless environment,
such that the probabilistic model indicates a probability
distribution for signal values at several locations in the wireless
environment. Alternatively, the model may insert new locations to a
single model based on a combination of existing locations. The
combination of submodels or existing locations includes combining
the inverse cumulative distribution functions of the submodels or
existing locations.
Inventors: |
Misikangas; Pauli;
(Helsinki, FI) ; Myllymaki; Petri; (Helsinki,
FI) |
Correspondence
Address: |
PILLSBURY WINTHROP SHAW PITTMAN, LLP
P.O. BOX 10500
MCLEAN
VA
22102
US
|
Assignee: |
Ekahau Oy
Helsinki
FI
|
Family ID: |
8564054 |
Appl. No.: |
11/653350 |
Filed: |
January 16, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10999140 |
Nov 30, 2004 |
7196662 |
|
|
11653350 |
Jan 16, 2007 |
|
|
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PCT/FI03/00413 |
May 27, 2003 |
|
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|
10999140 |
Nov 30, 2004 |
|
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Current U.S.
Class: |
455/446 ;
703/3 |
Current CPC
Class: |
H04W 64/00 20130101;
G01S 5/0252 20130101; G01S 3/06 20130101 |
Class at
Publication: |
455/446 ;
703/003 |
International
Class: |
H04Q 7/20 20060101
H04Q007/20; G06G 7/48 20060101 G06G007/48 |
Foreign Application Data
Date |
Code |
Application Number |
May 31, 2002 |
FI |
20021044 |
Claims
1. A model construction module for constructing a probabilistic
model of a wireless environment in which a target device is
operable to communicate using signals each of which has at least
one measurable signal value; the model construction module
comprising: a first software code portion for forming a plurality
of submodels of the wireless environment, each submodel indicating
a probability distribution for signal values at one or more
locations in the wireless environment; and for a second software
code portion for combining the submodels to a probabilistic model
of the wireless environment, the probabilistic model indicating a
probability distribution for signal values at several locations in
the wireless environment.
2. A model construction module according to claim 1, wherein the at
least one measurable signal value comprises bit error rate or
ratio.
3. A model construction module according to claim 1, further
comprising means for combining weighted probability distributions
of the submodels.
4. A model construction module for constructing a probabilistic
model of a wireless environment in which a target device is
operable to communicate using signals each of which has at least
one measurable signal value; the model construction module
comprising: a first software code portion for forming probabilistic
model of the wireless environment, the probabilistic model
indicating a probability distribution for signal values at several
locations in the wireless environment; a second software code
portion for inserting into the probabilistic model a probability
distribution for a new location, wherein the inserting step
comprises combining probability distributions for existing
locations.
5. A model construction module according claim 4, wherein the at
least one measurable signal value comprises bit error rate or
ratio.
Description
[0001] This is a divisional application of U.S. patent application
Ser. No. 10/999,140, filed Nov. 30, 2004, which a continuation of
International Application No. PCT/FI03/00413, filed May 27, 2003,
which claims priority from Finnish application No. 20021044, filed
May 31, 2002, the contents of all of which are incorporated herein
by reference.
BACKGROUND OF THE INVENTION
[0002] The invention relates generally to a positioning technique
in which a target device's location is estimated on the basis of
observations on the target device's wireless communication
environment. FIG. 1 schematically illustrates an example of such a
positioning technique. A target device T communicates via base
stations BS via a radio interface RI. In this example, the
communication is assumed to be radio communication. The target
device T observes signal values at the radio interface RI. The
observation set OS is applied to a probabilistic model PM that
models the target device's wireless communication environment and
produces a location estimate LE.
[0003] A practical example of the target device is a data
processing device communicating in a wireless local-area network
(WLAN) or a cellular radio network. The data processing device may
be a general-purpose laptop or palmtop computer or a communication
device, or it may be a dedicated test or measurement apparatus such
as a hospital instrument connected to the WLAN. A signal value, as
used herein, is a measurable and location-dependent quantity of a
fixed transmitter's signal. For example, signal strength and bit
error rate/ratio are examples or measurable and location-dependent
quantities. An example of a positioning technique that is based on
a probabilistic model of a device's radio environment is disclosed
in U.S. Pat. No. 6,112,095 to Mati Wax et al.
[0004] A problem underlying the invention is related to the fact
that such a probabilistic model works best when it is dense. This
means that the distance between sample points should not be too
high. A sample point is a point of the probabilistic model. In an
ideal case, the distance between sample points is equal to the
desired resolution of the probabilistic model, which means that the
sample point that best matches the target device's observations is
considered to be the target device's location. A problem is that
obtaining a large number of sample points by physical calibration
is time-consuming and expensive. This process is difficult to
perform automatically. As a result, some sample points should be
determined by deriving them from known calibrated locations, for
example, by interpolation. But, surprisingly, such interpolation is
far from trivial.
[0005] FIG. 2 illustrates a problem related to interpolation of
signal values. The independent variable x represents a measurable
signal value, such as signal strength. The dependent variable P(x)
is the probability of that signal value. FIG. 2 shows probability
distributions 21 and 22 for two locations Q.sub.1 and Q.sub.2,
respectively. To keep FIG. 2 simple, the probability distributions
21 and 22 are assumed to be non-overlapping. The signal values for
location Q.sub.1 are concentrated near value X.sub.1 and the signal
values for location Q.sub.2 are concentrated near value
X.sub.2.
[0006] Assume that we wish to predict signal values at a sample
point that is between the locations Q.sub.1 and Q.sub.2. For
example, we might wish to insert into the probabilistic model a
sample point that is between two locations for which actual
measurements or simulation results are available. An intuitive way
to create such a new sample point is to combine the probability
distributions 21 and 22 for locations Q.sub.1 and Q.sub.2. Curve
23, that is shown in a bold dash line, represents such a combined
(and normalized) probability distribution. But such a combined
probability distribution 23 does not predict signal values between
two locations, at least not very well. This is because the combined
probability distribution 23 has nonzero probability values only for
signal values that have nonzero probabilities in either of the
original probability distributions 21 and 22. Accordingly, the
intuitive way to combine the probability distributions 21 and 22
produces a result which is counter-intuitive and apparently false.
In FIG. 2, the signal value is quantified to discrete values, but
the result is the same if x is treated as a continuous
variable.
[0007] Thus a problem is how to create a sample point based on
interpolation of two or more known locations. This problem can be
generalized as follows: how to construct a probabilistic model that
models a target device's wireless environment for positioning the
target device, such that the probabilistic model can be constructed
on the basis of diverse information. The model may be based on
calibration measurements, simulations or theoretical calculations
or any combination thereof. The model should be generic enough to
be able to make best possible use of any information available.
BRIEF DESCRIPTION OF THE INVENTION
[0008] An object of the invention is to provide a method and an
apparatus for implementing the method so as to provide a solution
to the above-specified problem. In other words, it is an object of
the invention to provide a probabilistic model for a positioning
technique such that the probabilistic model can accept and combine
information from a variety of sources. Such information may be
calibration measurements, simulations or theoretical calculations
or any combination thereof. The calibration measurements may have
been made at different times, and a probabilistic model according
to the invention should be able to combine such information in a
sensible manner, instead of merely replacing old measurements with
new ones. The object of the invention is achieved by the methods
and equipment which are characterized by what is stated in the
independent claims. The preferred embodiments of the invention are
disclosed in the dependent claims.
[0009] The invention is based on the idea of forming a
probabilistic model based on simpler submodels or calibration
measurements such that the probabilistic model indicates a
probability distribution for signal values at several locations in
the wireless environment. A preferred embodiment of the invention
accomplishes the combination by combining the inverse cumulative
distribution functions of expected signal values. Persons with any
knowledge of probability theory will understand that many
mathematically equivalent techniques can be used, such as combining
the cumulative distribution functions (instead of inverse
cumulative distribution functions) and swapping the x and y axis. A
benefit of the invention is that a combination of the inverse
cumulative distribution functions results in a probabilistic model
that much better predicts signal values than does a model based on
combining the expected signal values themselves or their
probability distributions. For example, the invention can be used
to add new sample points to the probabilistic model based on two or
more locations for which calibration measurements or calculation or
simulation results exist. Such a creation of new sample points
based on existing calibration points can be called interpolation or
extrapolation, depending on whether the added sample point is
within or outside a line or area bounded by the existing
calibration points. Such an interpolation or extrapolation in
respect of locations can be called spatial interpolation or
extrapolation. In addition, the invention can be used for temporal
interpolation or extrapolation. That is, a new probabilistic model
can be created by combining two or more earlier probabilistic
models. A practical example of temporal interpolation or
extrapolation is that an updated probabilistic model is not only
based on the most recent measurements (or calculation or simulation
results) but a combination of the most recent and earlier
information. Yet further, the invention can be used to combine
different types of probabilistic models. A probabilistic model
created by a technique according to the invention can be based on
several types of information, including actual calibration
measurements and the results of simulations or theoretical
calculations, or any combination thereof. Interpolated or
extrapolated sample points can be created based on measured or
calculated sample points. The interpolation or extrapolation can be
spatial and/or temporal.
[0010] An aspect of the invention is a method for estimating a
target device's location, wherein the target device is operable to
move in a wireless environment and to communicate with the wireless
environment using signals each of which has at least one measurable
signal value. The method comprises:
[0011] a) forming a plurality of submodels of the wireless
environment, each submodel indicating a probability distribution
for signal values at one or more locations in the wireless
environment;
[0012] b) combining the submodels to a probabilistic model of the
wireless environment, the probabilistic model indicating a
probability distribution for signal values at several locations in
the wireless environment;
[0013] c) making a set of observations of signal values in the
wireless environment at the target device's location; and
[0014] d) estimating the target device's location based on the
probabilistic model and the set of observations.
[0015] Another aspect of the invention is a method that comprises
the steps of:
[0016] This is a continuation of International Application No.
PCT/FI03/00411, filed May 27, 2003, which claims priority from
Finnish application No. 20021045, filed May 31, 2002, the contents
of both of which are incorporated herein by reference.
[0017] a) forming probabilistic model of the wireless environment,
the probabilistic model indicating a probability distribution for
signal values at several locations in the wireless environment;
[0018] b) inserting into the probabilistic model a probability
distribution for a new location, wherein the inserting step
comprises combining probability distributions for existing
locations;
[0019] c) making a set of observations of signal values in the
wireless environment at the target device's location; and
[0020] d) estimating the target device's location based on the
probabilistic model and the set of observations.
[0021] The location-estimating step can be performed in the target
device. In this case, the target device must comprise the
probabilistic model and carry out the location-estimating software
routines. An advantage gained by performing the location-estimating
step in the target device is that the target device does not have
to transmit the signal value observations to have its location
estimated.
[0022] Alternatively, the location-estimating step can be performed
in an external location-estimating apparatus to which the target
device reports the sequence of observations via a radio network. An
advantage of this embodiment is that the target device does not
have to comprise the probabilistic model or the location-estimating
routines. However, it must send its observations to an external
location-estimating apparatus.
[0023] The measurable signal values preferably comprises signal
strength. Alternatively, or in addition to signal strength, the
measurable signal values may comprise bit error rate/ratio or
signal-to-noise ratio.
[0024] Yet another aspect of the invention is a model construction
module for constructing a probabilistic model of a wireless
environment in which a target device is operable to communicate
using signals each of which has at least one measurable signal
value. The model construction module has software code portions for
performing steps a) and b) of the first method.
[0025] Yet another aspect of the invention is a model construction
module for performing steps a) and b) of the second method.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] In the following the invention will be described in greater
detail by means of preferred embodiments with reference to the
attached drawings, in which
[0027] FIG. 1 schematically illustrates a positioning
technique;
[0028] FIG. 2 illustrates the problem underlying the invention;
[0029] FIG. 3 shows the principle of the invention;
[0030] FIGS. 4 and 5 illustrate interpolation in one or two
dimensions, respectively;
[0031] FIG. 6 illustrates a probabilistic model PM that is the
result of a combination of different submodels;
[0032] FIG. 7 shows a location estimation module LEM for estimating
the target device's location based on signal values at the radio
interface RI;
[0033] FIGS. 8A and 8B are block diagrams illustrating typical
target devices whose location is to be determined.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0034] FIG. 3 illustrates the principle of the invention. The
invention is based on the idea of combining the inverse cumulative
distribution functions of expected signal values at various
locations, instead of combining the signal values or their
probability distributions. FIG. 3 is drawn to the same scale as
FIG. 2, and the x-axis are aligned. Curve 31 represents the
cumulative distribution function for location Q.sub.1. For each
nonzero probability value in probability distribution 21, there is
a corresponding step in the cumulative distribution function 31.
Similarly, curve 32 represents the cumulative distribution function
for location Q.sub.2. Curve 33 is a cumulative distribution
function for a location Q.sub.X (such as a new sample point)
between locations Q.sub.1 and Q.sub.2. In this example, the new
sample point Q.sub.X is assumed to be in the middle of a straight
line from Q.sub.1 to Q.sub.2, and the cumulative distribution
function 33 is created by the following algorithm: for each of
several dependent variable values P(x), the independent variable
value x is determined by weighting with equal weights the
cumulative distribution functions 31 and 32 of the locations
Q.sub.1 and Q.sub.2, respectively.
[0035] Curve 34 is the expected probability distribution for the
new location (sample point) Q.sub.X. Curve 34 is aligned with
curves 21 and 22, and intuitively it seems that the probability
distribution 34 predicts signal values at Q.sub.4 much better than
does the probability distribution 23 shown in FIG. 2, because the
signal values with nonzero probabilities are somewhere between the
signal values X.sub.1 and X.sub.2, instead of being near either of
these values.
[0036] In FIG. 3, location Q.sub.X (such as a new sample point) was
assumed to be in the middle of a straight line from Q.sub.1 to
Q.sub.2. If this assumption is not valid, the cumulative
distribution function 33 for location Q.sub.X can be determined by
distance-weighting the cumulative distribution functions 31 and 32
of the locations Q.sub.1 and Q.sub.2 depending on the relative
distances from Q.sub.1 to Q.sub.X and from Q.sub.X to Q.sub.2.
FIGS. 4 and 5 illustrate such distance-weighting.
[0037] FIG. 4 illustrates interpolation along one dimension.
Q.sub.1, Q.sub.2 and Q.sub.X are three locations such that d.sub.1
is the distance from Q.sub.1 to Q.sub.X and d.sub.2 is the distance
from Q.sub.X to Q.sub.2. Ideally, the weights W.sub.1 and W.sub.2
for the cumulative distribution functions for locations Q.sub.1 and
Q.sub.2 should be selected such that W.sub.1d.sub.1=W.sub.2d.sub.2.
This weighting is illustrated by a horizontal bar 41 whose ends are
at Q.sub.1 and Q.sub.2 and the bar is pivoted at Q.sub.X (at
reference numeral 42). The bar is balanced if the weights W.sub.1
and W.sub.2 are inversely related to the distances d.sub.1 and
d.sub.2. Because the weights and distances are inversely related,
this weighting can be called inverse distance-weighting.
[0038] In addition to interpolation, the balanced-bar analogy can
be used with linear extrapolation as well. Assume an extrapolated
location Q.sub.X', for which a pivot point 43 for the balanced bar
is shown with a dashed line. The bar 41 can still be balanced by
using negative weights. Naturally, extrapolation is not reliable
with large distances.
[0039] FIG. 5 illustrates interpolation in two dimensions. On the
basis of three known locations Q.sub.A, Q.sub.B and Q.sub.C, we
wish to predict signal values for a new location Q.sub.Y. First the
cumulative distribution functions for each of the known locations
Q.sub.A, Q.sub.B and Q.sub.C are determined as shown in connection
with FIG. 3. Then an imaginary triangle is drawn such that its
apexes are at the known locations Q.sub.A, Q.sub.B and Q.sub.C. The
imaginary triangle is pivoted at the new location Q.sub.Y. Finally,
weights for the known locations Q.sub.A, Q.sub.B and Q.sub.C (the
apexes of the imaginary triangle) are selected such that the
triangle is balanced. (In FIGS. 4 and 5, the bar 41 and triangle 51
are assumed weightless.)
[0040] In general, the weights can be determined using vectors. Let
a be a vector from Q.sub.Y to Q.sub.A, b a vector from Q.sub.Y to
Q.sub.B and c a vector from Q.sub.Y to Q.sub.C. The weights
w.sub.A, w.sub.B and w.sub.C for locations Q.sub.A, Q.sub.B and
Q.sub.C can be obtained by solving the following pair of equations:
w.sub.A a+w.sub.B b+w.sub.C c= 0 [1] w.sub.A+w.sub.B+w.sub.C=1
[2]
[0041] In this 2-dimensional example, the solution can be found
easily by noting that the first equation is true only if the
weighted sum of x-coordinates of the vectors is zero as well as is
the weighted sum of y-coordinates. Thus, the weights are obtained
as the only solution of the following equation group:
w.sub.Aa.sub.x+w.sub.Bb.sub.x+w.sub.Cc.sub.x=0 [3]
w.sub.Aa.sub.y+w.sub.Bb.sub.y+w.sub.Cc.sub.y=0 [4]
w.sub.A+w.sub.B+w.sub.C=1 [5]
[0042] The method can be generalized for N-dimensional space. Let
v.sub.1, v.sub.2, . . . , v.sub.N+1 be the vectors and w.sub.1,
w.sub.2, . . . , w.sub.N+1 be the weights of the vectors. Note that
exactly N+1 vectors are needed to make the weights solvable. Now,
the weights are obtained as the only solution to the following pair
of equations: i = 1 N + 1 .times. w i v _ i = 0 _ [ 6 ] i = 1 N + 1
.times. w i = 1 [ 7 ] ##EQU1##
[0043] If a new location is formed on the basis of more than three
known locations, the area can be triangulated. For example,
Delaunay triangulation can be used, in which case the apexes of the
triangles are at the known locations. For each new location, the
triangle that either covers the new location (interpolation) or is
closest to the new location (extrapolation) is selected.
[0044] The technique of combining weighted cumulative distribution
functions is very generic. This technique can be used to:
[0045] 1. interpolate or extrapolate from known locations
(physically calibrated calibration points or simulated or
calculated locations);
[0046] 2. combine models of different age (instead of merely
replacing old data with new); and
[0047] 3. combine models of different type, such as models based on
physical calibration and models based on simulation or theoretical
calculations.
[0048] The generic nature of the technique suggests a novel
interpretation for a model. Instead of having one single model that
has several sample points, each of several models can have only one
sample point (calibrated, simulated or calculated), and the models
are then combined by combining their weighted cumulative
distribution functions. From this point on, the term probabilistic
model refers to the result of the combination, and the models on
the basis of which the probabilistic model is formed are called
submodels. Note that a probabilistic model itself can act as a
submodel for an updated probabilistic model.
[0049] Formally, a probabilistic model can be expressed as follows:
P(q|o).varies.P(o|q)P(q) [8]
[0050] where o denotes an observation (vector) and q is a location.
If the prior probability distribution P(q) is assumed to be uniform
(giving equal prior probability for all the locations q), then we
can see that the probability for a location q is proportional to
the probability that the model gives for the observation o at
location q. In other words, we can obtain a probability
distribution for a set of locations q by first computing the
probability of our observation o at each location, and then by
normalizing the resulting probabilities so that they sum up to one.
This means that the only thing we need to determine are the
conditional probability distributions P(o|q) at each location q.
One possibility for determining these probability distributions is
to assume the individual signal value observations o.sub.i to be
independent given the location q, in which case the individual
signal value probabilities are combined by simply multiplying them
as follows: P .function. ( o | q ) = i = 1 n .times. .times. P
.function. ( o i | q ) [ 9 ] ##EQU2##
[0051] In the real world, the signal value is a virtually
continuous variable, and the probability for any given signal value
is infinitesimal. Accordingly, the probability for a signal value
range [o.sub.i-.epsilon., o.sub.i+.epsilon.] should be used:
P([o.sub.i-.epsilon.,
o.sub.i+.epsilon.]|q)=F(o.sub.i+.epsilon.|q)-F(o.sub.i-.epsilon.|q)
[10] wherein F is the cumulative distribution function and
.epsilon. is a small constant.
[0052] FIG. 6 illustrates a probabilistic model PM that is the
result of such a combination of submodels. FIG. 6 shows submodels
of two different types. The first type is a calibration submodel.
This submodel is based on physical calibration measurements. The
second type is a propagation submodel and is based on modelling the
wireless communication environment by simulations or calculations.
Propagation submodels require very good knowledge of the
communication environment and the placement and properties of the
base stations. A propagation submodel can be created by a technique
analogous to visualizations based on ray-tracing. Lamps are
replaced by base stations, light-related properties (such as
reflection or retraction) are replaced by properties related to
radio signals, etc. Constructing a propagation submodel is
time-consuming, but once created, the model can produce a number of
sample points without physical measurements. On the other hand,
calibration measurements require no knowledge of the environment or
the base stations, and the measurements are relatively simple, but
they must be performed at small intervals and repeated
frequently.
[0053] Because the inventive technique supports combining many
different submodels, the calculations can be simplified by
assigning a submodel to each calibrated location. Reference
numerals 611 to 613 denote three such calibration submodels. Each
of the calibration submodels 611 to 613, by itself, is very
simplistic. For example, submodel 611 says that if F.sub.1 is the
best match for a signal value's observed probability distribution,
then the target device is located at location Q.sub.1. Each of the
calibration submodels 611 to 613 can be formally expressed by a
formula: F.sub.C(o|q)=F.sub.A(o) [11] in which F is the cumulative
distribution function of a signal value, o is an observation, q is
a location and A is the area covered by the submodel. The areas A
can be selected such that a submodel based on Q.sub.i covers the
entire area for which location Q.sub.i is the closest calibrated
location. In plain language, equation 11 says the function F is not
a function of location q after all but constant over the entire
area A. Reference numerals 621 to 623 denote older versions of the
calibration submodels 611 to 613.
[0054] Reference numeral 631 denotes a propagation model. A
propagation model can be formally expressed by a formula:
F.sub.P=F(o|q) [12]
[0055] Equation 12 means that the function F.sub.P for a
propagation model is location-dependent, that is, a function of
location q. The function F(o|q) can be a discrete function, which
means that the function values are calculated for several sample
points, or it can be a continuous function. A continuous function
can be formed by fitting a polynomial, spline or other suitable
curve to the calculated sample points.
[0056] The different models 611 to 631 can be combined by using the
following equation: F PM - 1 .function. ( o | q ) = i = 1 N .times.
F i - 1 .function. ( o | q ) W i .function. ( q ) i = 1 N .times. W
i .function. ( q ) [ 13 ] ##EQU3##
[0057] In equation 13, N is the number of submodels, F.sub.i.sup.-1
is the inverse function of the function F of submodel i, and
W.sub.i(q) is the weight assigned to submodel i at location q. Thus
the weights depend on location, as described in connection with
FIGS. 4 and 5. In plain language, equation 13 means that the
inverse function of the probabilistic model, namely
F.sub.PM.sup.-1, can be calculated by weighting and summing the
inverse function F.sub.i.sup.-1 of the function F.sub.i of each
submodel i. Then the weighted sum is normalized by dividing with
the sum of weights W.sub.i(q). The function FPM for the
probabilistic model PM is calculated by taking the inverse of the
inverse function F.sub.PM.sup.-1. The calculation of equation 13
should be repeated for each channel. It should also be repeated for
each signal value type, such as signal strength, bit error
rate/ratio, signal-to-noise ratio, etc.
[0058] The probabilistic model PM shown in FIG. 6 comprises a
function F (probability distribution) for several sample points, of
which five are shown, namely Q.sub.1, Q.sub.2, Q.sub.3, Q.sub.X and
Q.sub.Y. Reference numerals 641 to 645 denote five pairs of a
sample point Q.sub.i and corresponding function F.sub.i at that
point. In this example, sample points Q.sub.1, Q.sub.2, and Q.sub.3
are calibrated locations, that is, there is a respective
calibration submodel 611 to 613. Sample points Q.sub.X and Q.sub.Y
are points for which actual calibration measurements are not
available, and the corresponding functions F.sub.X and F.sub.Y are
derived by interpolation/extrapolation from the calibration
submodels 611 to 623 and/or from the propagation model 631.
[0059] Equation 13 is calculated for each of the sample points 641
to 645 in the probabilistic model. What remains to be done is
assigning the relative weights W.sub.i. Because equation 13 is
normalized, the absolute values of the weights are immaterial; it
is the relative weights that matter. Reference numeral 65 generally
denotes the weights W.sub.i. Four different weights are shown
schematically. A thick arrow represents a large weight, a thin
arrow represents a medium weight and a dashed arrow represents a
small weight. A missing arrow means a zero weight. The weights are
selected on the basis of some confidence level, wherein the
confidence level is a measure of the capability of the submodel to
predict the function F.sub.i at that sample point. For example,
reference 641 denotes function F.sub.1 for sample point Q.sub.1 In
this example, because a calibration submodel 611 exists for sample
point Q.sub.1, and the submodel 611 is assumed to be very recent,
the confidence level of submodel 611 is high, and the function
F.sub.1 for sample point Q.sub.1 is determined only on the basis of
the submodel 611. Reference 642 denotes function F.sub.2 for sample
point Q.sub.2. Here the assumption is that the most recent
calibration submodel 612 has a large weight and the previous
calibration submodel 622 has a small weight. Reference 643 denotes
function F.sub.3 for sample point Q.sub.3. Function F.sub.3 is
strongly influenced by the corresponding submodel 613 and weakly
influenced by the previous calibration submodel 623. It is also
weakly influenced by the propagation model 631.
[0060] Reference numerals 644 and 645 respectively denote sample
points Q.sub.X and Q.sub.Y for which actual calibration
measurements are not available. In this example, sample point
Q.sub.X (reference 644) is determined solely by interpolation on
the basis of the three calibration submodels 611 to 613. Sample
point Q.sub.X is assumed substantially equidistant from the
calibrated locations Q.sub.1, Q.sub.2, and Q.sub.3, and the
relative weights are approximately equal. Sample point Q.sub.Y
(reference 645) is assumed to be near Q.sub.3, and the relative
weight is strong, but the corresponding function F.sub.Y is also
influenced by the propagation model 631 and the calibration
submodel 612 for location Q.sub.2.
[0061] FIG. 7 is a block diagram of an exemplary location
estimation module LEM for estimating the target device's location
based on signal values at the radio interface RI. FIG. 7 shows a
compact location estimation module LEM, but more distributed
embodiments are equally possible. An essential feature of the
location estimation module is a probabilistic model PM of the
target device's wireless environment, the probabilistic model being
able to predict the target device's location given a plurality of
observations from the radio interface. In this example, the
probabilistic model PM is built and maintained by a model
construction module MCM. The model construction module MCM builds
and maintains the probabilistic model on the basis of calibration
data CD or propagation data PD in the form of one or more
propagation models, or any combination thereof. Calibration data CD
is the result of physically measuring signal values at known
locations (or determining the coordinates of those locations if
they are not known by other means). Optionally, the calibration
data records may also comprise the time at which the measurement
was made, in case the signal parameters vary with time. Instead of
the calibration data CD, or in addition to them, one or more
propagation models PD can be used to model the radio interface RI.
The propagation models can be constructed by techniques that are
analogous to ray-tracing techniques for visual simulation. The
locations at which calibration measurements are collected are
called calibration points. The calibration data CD comprises data
records each of which comprises the location of the calibration
point in question and the set of signal parameters measured at that
calibration point. The location can be expressed in any absolute or
relative coordinate system. In special cases, such as trains,
highways, tunnels, waterways or the like, a single coordinate may
be sufficient, but normally two or three co-ordinates will be
used.
[0062] There is also a location calculation module LCM for
producing a location estimate LE on the basis of the target
device's observation set OS and the probabilistic model PM. For
instance, the location calculation module can be implemented as a
software program being executed in a laptop or palmtop computer.
Technically, the `measurements` and `observations` can be performed
similarly, but to avoid confusion, the term `measurement` is
generally used for the calibration measurements, and the signal
parameters obtained at the current location of the target device
are called `observations`. The target device's most recent set of
observations is called current observations.
[0063] FIG. 8A is a block diagram illustrating a typical target
device T whose location is to be determined. In this example, the
target device T is shown as a portable computer that communicates
via a radio network RN. For example, the radio network can be WLAN
(wireless local-area network) network. In the embodiment shown in
FIG. 8A, the location estimation module LEM comprising the
probabilistic model PM is not installed in the target device T. As
a result, the target device T must send its observation set OS to
the location estimation module LEM via one or more of the base
station BS it is connected to. The location estimation module LEM
returns the target device its location estimate LE via the radio
interface RI.
[0064] FIG. 8B shows an alternative embodiment in which the target
device's attached computer PC receives a copy of the probabilistic
model PM on a detachable memory DM, such as a CD-ROM disk, and the
target device T is able to determine its own location without
transmitting anything. As a yet further alternative (not shown
separately), the attached computer PC may receive the probabilistic
model via an Internet (or any other data) connection to the
location estimation module LEM. Wideband mobile stations can
receive the probabilistic model via the radio interface RI. A
hybrid of the technologies may also be used such that the receiver
receives an initial probabilistic model via a wired connection or
on the detachable memory, but later updates to the model are sent
via the radio interface.
[0065] It is readily apparent to a person skilled in the art that,
as the technology advances, the inventive concept can be
implemented in various ways. The invention and its embodiments are
not limited to the examples described above but may vary within the
scope of the claims.
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