Patent Application
20110189960
The present invention relates to wireless telecommunications in general, and, more particularly, to methods and systems for estimating whether a wireless terminal is indoors.
The salient advantage of wireless telecommunications over wireline telecommunications is the mobility that is afforded to the users of the wireless telecommunications system. On the other hand, the salient disadvantage of wireless telecommunications lies in that fact that because the user is mobile, an interested party might not be able to readily ascertain the location of the user.
Such interested parties might include both the user of the wireless terminal and remote parties. There are a variety of reasons why the user of a wireless terminal might be interested in knowing his or her own location. For example, the user might be interested in telling a remote party where he or she is.
There are a variety of reasons why a remote party might be interested in knowing the location of the user. For example, the recipient of a 911 emergency call from a wireless terminal might be interested in knowing the location of the wireless terminal so that emergency services vehicles can be dispatched to that location.
A variety of techniques are known in the prior art for estimating the location of a wireless terminal.
In accordance with one technique, the location of a wireless terminal is estimated to be at the center of the cell in which the wireless terminal is located. This technique is advantageous in that it does not require that additional hardware be added to the wireless terminal or to the wireless telecommunications system, and this means that the first technique can be inexpensively implemented in legacy systems. The first technique is only accurate, however, to a few kilometers, and, therefore, it is generally not acceptable for applications (e.g., emergency services dispatch, etc.) that require higher accuracy.
In accordance with a second technique, the location of a wireless terminal is estimated by triangulating the angle of arrival or the time of arrival of the signals transmitted by the wireless terminal to be located at various receivers. This technique is accurate to within a few hundreds of meters and is advantageous in that it can be used with legacy wireless terminals. It is disadvantageous, however, in that it generally requires that hardware be added to the telecommunication system's base stations, and this is very expensive.
In accordance with a third technique, the location of a wireless terminal is estimated by a radio navigation unit, such as a Global Positioning System (GPS) receiver, that is incorporated into the wireless terminal. This technique is accurate to within tens of meters and is advantageous in that it does not require that additional hardware be added to the telecommunication system's infrastructure. Two disadvantages of GPS, however, is that it cannot be used with legacy wireless terminals that do not comprise a radio navigation unit, and it often fails to work indoors.
In some instances it might be desirable to know whether a wireless terminal is located indoors. In some such instances, such as when emergency parties are dispatched to a distressed caller, it might be desirable to have this information in addition to an estimate of the location of the wireless terminal—particularly when the estimate is imprecise—while in some other instances this information might be sufficiently useful without a location estimate.
Naturally if there is a mechanism for obtaining an accurate estimate of the location of a wireless terminal (e.g., Global Positioning System [GPS], etc.) and the geographical boundaries of buildings are known, then it is a straightforward matter to determine whether the wireless terminal is indoors or outdoors. However, information about the locations of buildings might not always be available. Moreover, a wireless terminal might lack the hardware or software required for obtaining an accurate location estimate. Finally, even when a wireless terminal does in fact have the requisite hardware and software, location estimation techniques such as GPS and triangulation can work poorly when the wireless terminal is indoors, due to signal penetration loss.
What is needed, therefore, is a technique for estimating whether a wireless terminal is indoors that is capable of overcoming these difficulties.
The present invention provides a technique for estimating whether or not a wireless terminal is indoors. In particular, the illustrative embodiment employs a pattern classifier (e.g., a neural network, a decision tree, a support vector machine [SVM], etc.) that is trained via a learning algorithm on a plurality of input/output mappings. In accordance with the illustrative embodiment, each mapping corresponds to a respective location, where the output of the mapping is a Boolean value that indicates whether the location is indoors, and the input of the mapping is based on the following empirical data for the location:
During training, the pattern classifier learns to distinguish between the locations of the training set that are indoors and those that are outdoors. When the training procedure is effective, the pattern classifier also learns to predict, with relatively high accuracy, whether other locations not in the training set are indoors or outdoors. This prediction capability, known as generalization, is a result of the pattern classifier inferring rules and identifying patterns during training.
In accordance with the illustrative embodiment, after the pattern classifier has been trained, a computer-executable program is generated based on the trained pattern classifier. At run-time the computer-executable program receives empirical data reported by a wireless terminal and a location estimate for the terminal that might be crude or inaccurate (e.g., based on Cell Identifier [Cell-ID], based on the Global Positioning System [GPS], etc.), and estimates whether or not the terminal is indoors based on this information.
The illustrative embodiment comprises: estimating whether or not a wireless terminal is indoors based, at least in part, on: (i) the number of base stations whose transmissions are identified at the wireless terminal, (ii) one or more signal-strength measurements of one or more base-station-transmitted signals at the wireless terminal, (iii) the predicted number of base stations whose transmissions are identifiable at the wireless terminal, and (iv) one or more predicted signal strengths of one or more base-station-transmitted signals at the wireless terminal.
a depicts the distortion function D(A,K,Q) for one trait and for an ideal radio.
b depicts a graph of the distortion function of an illustrative real-world radio.
c depicts a graph of the calibration function C(R,K,Q) for the distortion function D(A,K,Q) depicted in
For the purposes of this specification, the following terms and their inflected forms are defined as follows:
In accordance with the illustrative embodiment, wireless telecommunications service is provided to wireless terminal 201 in accordance with the Universal Mobile Telecommunications System, which is commonly known as “UMTS.” After reading this disclosure, however, it will be clear to those skilled in the art how to make and use alternative embodiments of the present invention that operate in accordance with one or more other air-interface standards (e.g., Global System Mobile “GSM,” CDMA-2000, IS-136 TDMA, IS-95 CDMA, 3G Wideband CDMA, IEEE 802.11 WiFi, 802.16 WiMax, Bluetooth, etc.) in one or more frequency bands.
Wireless terminal 201 comprises the hardware and software necessary to be UMTS-compliant and to perform the tasks described below and in the accompanying figures. Wireless terminal 201 is mobile and can be at any indoors or outdoors location within geographic region 120. Although wireless telecommunications system 200 comprises only one wireless terminal, it will be clear to those skilled in the art, after reading this disclosure, how to make and use alternative embodiments of the present invention that comprise any number of wireless terminals.
Base stations 202-1, 202-2, and 202-3 communicate with wireless switching center 211 and with wireless terminal 201 via radio in well-known fashion. As is well known to those skilled in the art, base stations are also commonly referred to by a variety of alternative names such as access points, nodes, network interfaces, etc. Although the illustrative embodiment comprises three base stations, it will be clear to those skilled in the art, after reading this disclosure, how to make and use alternative embodiments of the present invention that comprise any number of base stations.
In accordance with the illustrative embodiment of the present invention, base stations 202-1, 202-2, and 202-3 are terrestrial, immobile, and within geographic region 120. It will be clear to those skilled in the art, after reading this disclosure, how to make and use alternative embodiments of the present invention in which some or all of the base stations are airborne, marine-based, or space-based, regardless of whether or not they are moving relative to the Earth's surface, and regardless of whether or not they are within geographic region 120.
Wireless switching center 211 comprises a switch that orchestrates the provisioning of telecommunications service to wireless terminal 201 and the flow of information to and from server 210, as described below and in the accompanying figures. As is well known to those skilled in the art, wireless switching centers are also commonly referred to by other names such as mobile switching centers, mobile telephone switching offices, routers, etc.
Although the illustrative embodiment comprises one wireless switching center, it will be clear to those skilled in the art, after reading this disclosure, how to make and use alternative embodiments of the present invention that comprise any number of wireless switching centers. For example, when a wireless terminal can interact with two or more wireless switching centers, the wireless switching centers can exchange and share information that is useful in estimating the location of the wireless terminal. For example, the wireless switching centers can use the IS-41 protocol messages HandoffMeasurementRequest and HandoffMeasurementRequest2 to elicit signal-strength measurements from one another. The use of two or more wireless switching centers is particularly common when the geographic area serviced by the wireless switching center is small (e.g., local area networks, etc.) or when multiple wireless switching centers serve a common area.
In accordance with the illustrative embodiment, all of the base stations servicing wireless terminal 201 are associated with wireless switching center 211. It will be clear to those skilled in the art, after reading this disclosure, how to make and use alternative embodiments of the present invention in which any number of base stations are associated with any number of wireless switching centers.
In accordance with the illustrative embodiment, wireless switching center 211 is outside of geographic region 120. It will be clear to those skilled in the art, after reading this disclosure, how to make and use alternative embodiments of the present invention in which some or all of wireless switching center 211 is instead within geographic region 120.
Server 210 is a data-processing system that is capable of:
Database 220 stores measured and predicted signal traits for a plurality of locations in geographic region 120, as described in detail below and with respect to
Column 301 stores the latitude of each location in geographic region 120.
Column 302 stores the longitude of each location in geographic region 120.
Column 303 stores the number of base stations whose transmissions are identified at each location in geographic region 120, as determined empirically (e.g., as reported by a wireless terminal, as reported during a drive test through geographic region 120, etc.). In other words, the values of column 303 indicate how many base stations are empirically “visible” at each location. As will be appreciated by those skilled in the art, in some embodiments of the present invention the contents of column 303 might also indicate, in addition to the number of visible base stations at the location, how many of these visible base stations actively communicate with a wireless terminal at that location.
Column 304 stores signal-strength measurements of base-station-transmitted signals at each location in geographic region 120, as determined empirically.
Column 305 stores interference measurements of base-station-transmitted signals at each location in geographic region 120, as determined empirically.
Column 306 stores predicted signal strengths of base-station-transmitted signals at each location in geographic region 120. In accordance with the illustrative embodiment, the predicted signal strengths are obtained from a pre-computed propagation map that models all locations as outdoors locations. As will be appreciated by those skilled in the art, in some other embodiments of the present invention the predicted signal strengths might be determined in some other manner (e.g., via a mathematical model, from a different type of propagation map, etc.).
Column 307 stores the predicted number of base stations whose transmissions are identifiable (i.e., the predicted number of “visible” base stations) at each location in geographic region 120. As will be appreciated by those skilled in the art, the predicted number of visible base stations might be determined in a variety of ways, such as via ray tracing, propagation analysis, mathematical modeling, etc.
Column 308 stores a value that indicates whether each location is indoors or outdoors. As will be appreciated by those skilled in the art, in some embodiments of the present invention the values of column 308 might be Boolean, while in some other embodiments column 308 might store a non-Boolean value, such as a real number in [0,1] corresponding to a probability or confidence metric, instead of, or perhaps in addition to, a Boolean value.
As will be appreciated by those skilled in the art, in some other embodiments of the present invention, database 220 might represent information in a different manner (e.g., location might be represented by Cartesian coordinates, etc.), or might omit one or more empirical data of the illustrative embodiment, or might omit one or more predicted data of the illustrative embodiment, or might store other empirical data in addition to those of the illustrative embodiment, or might store other predicted data in addition to those of the illustrative embodiment, and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such alternative embodiments of database 220. As will further be appreciated by those skilled in the art, in some other embodiments of the present invention, database 220 might employ an alternative schema for storing and organizing information, and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such alternative embodiments of database 220.
In accordance with the illustrative embodiment, the tasks of the method of
At task 401, server 210 populates database 220; task 401 is described in detail below and with respect to
At task 402, server 210 generates a training set S of input/output mappings based on the contents of database 220, where the input of each mapping is a vector based on:
In accordance with the illustrative embodiment, the input vector comprises the following elements:
As will be appreciated by those skilled in the art, the elements of the input vector might be represented in a variety of ways (e.g., normalized values, raw values, quantized values, etc.). As will further be appreciated by those skilled in the art, in some other embodiments of the present invention one or more of the input elements of the illustrative embodiment might be omitted, or one or more different input elements might be employed in addition to those of the illustrative embodiments (e.g., timing information, percentage of indoor points at possible locations etc.), or both, and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such alternative embodiments. As will yet further be appreciated by those skilled in the art, some other embodiments of the present invention might model location estimation errors in an alternative manner (e.g., use an area other than a circle, etc.), or might not use location estimation errors at all (i.e., by omitting the additional rows of item (ii) above), and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such alternative embodiments. Moreover, it is well-known in the art that the extraction of a training set from a collection of data, as well as determining which inputs to feed into a pattern classifier and an appropriate data representation scheme, are an art in itself and critical for effective training.
At task 403, server 210 trains a pattern classifier P on training set S in accordance with an appropriate learning algorithm, in well-known fashion. In accordance with the illustrative embodiment of the present invention, pattern classifier P is a type of pattern classifier known in the art as a support vector machine (SVM). As will be appreciated by those skilled in the art, a variety of other types of pattern classifiers might be used instead (e.g., a neural network, a decision tree, a genetic algorithm, etc.), and it is well-known in the art how to train such pattern classifiers.
At task 404, server 210 generates a computer-executable program X that: accepts a set of inputs, generates an input vector based on the set of inputs, simulates the execution of trained pattern classifier P on the input vector, and outputs the output value generated by pattern classifier P. It is well known in the art how to generate such a computer-executable program based on a given pattern classifier.
At task 405, computer-executable program X is launched at server 210. As noted above, in some other embodiments of the present invention, computer-executable program X might be deployed and launched at some other entity (e.g., wireless switching center 211, a data-processing system not depicted in
At subtask 501, server 210 receives empirical data for a plurality of locations including, for each location: the number of base stations from which transmissions are identified (i.e., the number of visible base stations at each location, as determined empirically); signal-strength measurements of base-station-transmitted signals at each location; interference measurements of base-station-transmitted signals at each location; and optionally, the number of visible base stations that a wireless terminal actively communicates with at each location.
As will be appreciated by those skilled in the art, these measurements often have errors, some of which might be due to predictable factors, and some of which might be due to unpredictable factors. When the nature or magnitude of the measurement errors is unpredictable, there is little that can be done to compensate for them. However, measurement errors that are predictable can be corrected, and it is well-known in the art that the nature and magnitude of some measurement errors are in fact predictable. For example, the radio used to obtain the measurements might be known to incorrectly measure and report the strength of signals by a fixed amount (say, −2 dB). Consequently, it is advantageous, if possible, to correct such systemic errors prior to training pattern classifier P on the measurements. A technique for doing so based on distortion functions is now described below and with respect to
The distortion function D(A,K,Q) for a particular radio relates the reported measurement R for a trait Q (e.g., signal strength, to the actual value A for that trait and the defining characteristic K of the radio making the measurement:
R=D(A,K,Q). (Eq. 1)
In accordance with the illustrative embodiment, the distortion function D(A,K,Q) for the radio that measures and reports the signal trait values is provided by the radio manufacturer. It will be clear to those skilled in the art, however, after reading this disclosure, how to generate the distortion function D(A,K,Q) for any radio without the assistance of the radio manufacturer.
An ideal radio perfectly measures and reports the value of the traits it receives; the distortion function D(A,K,Q) for one trait and for an ideal radio is depicted in
A real-world radio, in contrast, typically does not perfectly measure the traits of the signals they receive. This is particularly true for measurements of signal strength where the errors can be large. For example,
The nature and magnitude of each of the errors in the reported measurements is inherent in the distortion function D(A,K,Q), and, therefore, knowing the distortion function, a calibration function, denoted C(R,K,Q), can be derived from the distortion function. Once obtained, the calibration function can be applied to the measurements to eliminate the distortion errors and thereby recover the actual values of the traits.
In general, the calibration function C(R,K,Q) relates the calibrated measurement of a trait Q to the reported measurement R of trait Q and the defining characteristic K of the radio making the measurement:
R′=D(R,K,Q) (Eq. 2)
where R′ denotes the calibrated value of measurement R.
The calibration function C(R,K,Q) is the inverse of the distortion function D(A,K,Q). In other words, the salient characteristic of the calibration function C(R,K,Q) is that it satisfies the equation:
R′=A=C(D(A,K,Q),K,Q) (Eq. 3)
so that the calibrated measurement, R′, is what the reported measurement, R, would have been had the radio making and reporting the measurement been ideal.
c depicts a graph of the calibration function C(R,K,Q) for the distortion function D(A,K,Q) depicted in
At subtask 502, server 210 corrects the measurements received at subtask 501, if necessary, based on the radio that obtained the measurements, as described above.
At subtask 503, server 210 determines predicted (i.e., theoretical) signal strengths and base station visibility for the plurality of locations. In accordance with the illustrative embodiment, the predicted signal strengths are obtained from a pre-computed propagation map that models all locations as outdoors locations. As will be appreciated by those skilled in the art, in some other embodiments of the present invention the predicted signal strengths might be determined in some other manner (e.g., via a mathematical model, from a different type of propagation map, etc.). Similarly, as will be appreciated by those skilled in the art, predicted base station visibility (i.e., the predicted number of base stations whose transmissions are identifiable at a location) might also be determined in a variety of ways, such as via ray tracing, propagation analysis, mathematical modeling, etc. As will further be appreciated by those skilled in the art, in some embodiments of the present invention the empirical data obtained at subtask 501 might be utilized for refining the predictive estimates at subtask 503, as well as for training the pattern classifier at task 403.
At subtask 504, server 210 populates table 300 of database 220 with the (possibly corrected) empirical data obtained at subtasks 501 and 502 and the predicted data obtained at subtask 503, in well-known fashion. After subtask 504, task 401 is completed and execution proceeds to task 402.
At task 701, computer-executable program X receives:
At task 702, computer-executable program X corrects, if necessary, one or more of the data received at task 701.
In accordance with the illustrative embodiment, a calibration table is employed that stores the values of a plurality of calibration functions C(R,K,Q) for various combinations of reported measurement R, characteristic K and trait Q. For example, for the case in which items (1) through (3) are measured and reported by the wireless terminal, a calibration table such as the one shown below in Table 1 might be employed.
The purpose of the characteristic, K, is to identify which calibration function should be used in calibrating the reported measurements from the wireless terminal, and, therefore, the characteristic, K, should be as indicative of the actual calibration function for the wireless terminal as is economically reasonable.
For example, the characteristic, K, can be, but is not limited to:
The most accurate characteristic is the unique identity of the particular wireless terminal, because the calibration function generated for that very wireless terminal could then be used to correct the measurement errors at the terminal. It is unlikely, however, that this is economically feasible, because it would require that every wireless terminal be tested to determine its own unique distortion function.
On the other hand, using only the make of the wireless terminal as the characteristic, K, is economically reasonable, but it is unlikely that a single calibration function for all of a manufacturer's wireless terminals would provide very accurate calibrated signal-strength measurements.
As a compromise, the illustrative embodiment uses the combination of make and model of the wireless terminal as the characteristic, K, because it is believed that the amount of variation between wireless terminals of the same make and model will be small enough that a single calibration function for that model should provide acceptably accurate calibrated measurements for every wireless terminal of that make and model.
As will be appreciated by those skilled in the art, some other embodiments of the present invention might employ characteristics K for calibration function C(R,K,Q) that differ from those of the illustrative embodiment, or might represent calibration function C(R,K,Q) in a manner other than tabular form, or might in fact do away with task 702 altogether (i.e., not even bother with correcting the reported measurements), and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such embodiments.
At task 703, computer-executable program X receives a location estimate for the wireless terminal. As will be appreciated by those skilled in the art, the location estimate received at task 703 might be obtained via any one of a variety of possible techniques, such as via Cell Identifier (Cell-ID), GPS, radio frequency (RF) fingerprinting, triangulation, etc.; moreover, in some embodiments of the present invention, the location estimate might originate from the wireless terminal, while in some other embodiments, the location estimate might originate from another entity (e.g., wireless switching center 211, one or more base stations 102-i, a location server or other type of data-processing system not depicted in
At task 704, computer-executable program X looks up in table 300 of database 220 for each of the estimated location(s):
At task 705, computer-executable program X generates values based on items (1) through (5), and feeds the values to its implementation of trained pattern classifier P. In accordance with the illustrative embodiment, the generated values comprise:
At task 706, computer-executable program X outputs the indoors/outdoors flag generated by its implementation of trained pattern classifier P. After task 706, execution of computer-executable program X continues back at task 701 for the next iteration.
It is to be understood that the disclosure teaches just one example of the illustrative embodiment and that many variations of the invention can easily be devised by those skilled in the art after reading this disclosure and that the scope of the present invention is to be determined by the following claims.
