Claims:WE CLAM:

1. A processor implemented method, comprising:
(i) receiving, by a system, a first set and a second set of Received Signal Strength Indicator (RSSI) measurements from a plurality of Bluetooth low energy (BT-LE) beacons from a region of interest pertaining to a location having a first predetermined distance range, wherein each beacon from said plurality of beacons is positioned in said region of interest, wherein each beacon is separated from another beacon by a predetermined distance;
(ii) normalizing, for a second predetermined distance range, said first set and said second set of Received Signal Strength Indicator (RSSI) measurements received from said plurality of beacons to obtain a maximum tolerance based RSSI measurement and a minimum tolerance based RSSI measurement;
(iii) extracting, using said maximum tolerance based RSSI measurement and said minimum tolerance based RSSI measurement, one or more features from a first subset present in said set of normalized RSSI measurements by applying a slide bulging window technique on said first subset, and repeating the step (iii) until a last subset is reached in said second predetermined distance range to obtain a first set of extracted features; and
(iv) training, a classifier of said system with a pre-configured value, using said first set of extracted features to obtain training data.

2. The processor implemented method of claim 1, further comprising receiving a third set and a fourth set of RSSI measurements from a plurality of BT-LE beacons transmitted by a client device associated with a user; and performing the steps (ii) till (iii) to obtain a second set of extracted features.

3. The processor implemented method of claim 2, further comprising comparing said second set of extracted features with said training data.

4. The processor implemented method of claim 3, further comprising
estimating, based on said comparison of said second set of extracted features with said training data, a distance of said user from each of said plurality of beacons; and
localizing said user in said region of interest pertaining to said location based on said estimated distance.

5. The processor implemented method of claim 1, wherein said first set of extracted features comprise at least one of a maximum normalized RSSI measurement, a minimum normalized RSSI measurement, a mean of normalized RSSI measurements, a median of normalized RSSI measurements and a standard deviation of normalized RSSI measurements.

6. The processor implemented method of claim 4, further comprising
determining an estimated distance of one or more users from a plurality of beacons by obtaining subsequent sets of RSSI measurements and normalizing said subsequent set of RSSI measurements; and
localizing, using a mode of said normalized RSSI measurements, or a median of said normalized RSSI measurements, said one or more users based on the determined estimated distance.

7. The processor implemented method of claim 1, wherein said first set and said second set of Received Signal Strength Indicator (RSSI) measurements are equal in number.

8. The processor implemented method of claim 1, wherein said first set of RSSI measurements comprises a line of sight RSSI measurements, and wherein said second set of RSSI measurements comprises a non-line of sight of RSSI measurements.

9. A system, comprising:
a memory storing instructions;
one or more communication interfaces;
one or more hardware processors coupled to said memory through the one or more communication interfaces, wherein said one or more hardware processors are configured by said instructions to:
(i) receive a first set and a second set of Received Signal Strength Indicator (RSSI) measurements from a plurality of Bluetooth low energy (BT-LE) beacons from a region of interest pertaining to a location having a first predetermined distance range, wherein each beacon from said plurality of beacons is positioned in said region of interest such that each beacon is separated from another beacon by a predetermined distance;
(ii) normalize, for a second predetermined distance range, said first set and said second set of Received Signal Strength Indicator (RSSI) measurements received from said plurality of beacons to obtain a maximum tolerance based RSSI measurement and a minimum tolerance based RSSI measurement;
(iii) extract, using said maximum tolerance based RSSI measurement and said minimum tolerance based RSSI measurement, one or more features from a first subset present in said set of normalized RSSI measurements by applying a slide bulging window technique on said first subset, and repeating the step (iii) until a last subset is reached in said second predetermined distance range to obtain a first set of extracted features; and
(iv) train a classifier of said system with a pre-configured value, using said set of extracted features to obtain training data.

10. The system of claim 9, wherein said set of extracted features comprise at least one of a maximum normalized RSSI measurement, a minimum normalized RSSI measurement, a mean of normalized RSSI measurements, a median of normalized RSSI measurements and a standard deviation of normalized RSSI measurements.

11. The system of claim 9, wherein said one or more hardware processors are further configured to
receive a third set and a fourth set of RSSI measurements from a plurality of BT-LE beacons transmitted by a client device associated with a user, and
perform the steps (ii) till (iii) to obtain a second set of extracted features.

12. The system of claim 11, wherein said one or more hardware processors are further configured to
perform a comparison said second set of extracted features with said training data,
estimate, based on said comparison of said second set of extracted features with said training data, a distance of said user from each of said plurality of beacons, and
localize said user in said region of interest pertaining to said location using said estimated distance.

13. The system of claim 12, wherein said one or more hardware processors are further configured to
determine an estimated distance of one or more users from a plurality of beacons by obtaining subsequent sets of RSSI measurements and normalizing said subsequent set of RSSI measurements; and
localize, using a mode of said normalized RSSI measurements, or a median of said normalized RSSI measurements, said one or more users based on the determined estimated distance.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEMS AND METHODS FOR DISTANCE ESTIMATION AND LOCALIZATION OF USERS USING BLUETOOTH LOW ENERGY BEACONS

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the embodiments and the manner in which it is to be performed.
TECHNICAL FIELD
[0001] The embodiments herein generally relate to localization techniques, and, more particularly, to systems and methods for distance estimation and localization of users using Bluetooth low energy beacons.

BACKGROUND
[0002] Localization using both radio frequency (RF) and Wi-Fi on mobile communication devices is a common approach. Localization and mapping are performed to compute the most probable location using sensors. Current and conventional systems and methods used in Wi-Fi for trilateration or localization use the power decay model which expects logarithmic decay of Received Signal Strength Indicator (RSSI) with respect to distance from the source. However, for Bluetooth low energy BT-LE, it is found that such techniques are prone to errors even after rigorous regression of equation parameters since these are not accurate as the RSSI values are not stable over time. Existing solutions use visibility and proximity profiles of the BT-LE for localization, which require dense RF environments. However, these existing solutions do not provide a uniform positioning technique and hence may lead to inaccuracy in localization.

SUMMARY
[0003] The following presents a simplified summary of some embodiments of the disclosure in order to provide a basic understanding of the embodiments. This summary is not an extensive overview of the embodiments. It is not intended to identify key/critical elements of the embodiments or to delineate the scope of the embodiments. Its sole purpose is to present some embodiments in a simplified form as a prelude to the more detailed description that is presented below. In view of the foregoing, an embodiment herein provides systems and methods for distance estimation and localization of users using Bluetooth low energy beacons.

[0004] In one aspect, a processor implemented method is provided. The method comprising: (i) receiving, by a system, a first set and a second set of Received Signal Strength Indicator (RSSI) measurements from a plurality of Bluetooth low energy (BT-LE) beacons from a region of interest pertaining to a location having a first predetermined distance range, wherein each beacon from the plurality of beacons is positioned in the region of interest such that each beacon is separated from another beacon by a predetermined distance. In an embodiment, the first set and the second set of Received Signal Strength Indicator (RSSI) measurements are equal in number. In another embodiment, the first set of RSSI measurements comprises a line of sight RSSI measurements, and the second set of RSSI measurements comprises a non-line of sight of RSSI measurements.

[0005] The method further includes the steps of (ii) normalizing, for a second predetermined distance range, the first set and the second set of Received Signal Strength Indicator (RSSI) measurements received from the plurality of beacons to obtain a maximum tolerance based RSSI measurement and a minimum tolerance based RSSI measurement, (iii) extracting, using the maximum tolerance based RSSI measurement and the minimum tolerance based RSSI measurement, one or more features from a first subset present in the set of normalized RSSI measurements by applying a slide bulging window technique on the first subset, and repeating the step (iii) until a last subset is reached in the second predetermined distance range to obtain a first set of extracted features. In an embodiment, the first set of extracted features may comprise at least one of a maximum normalized RSSI measurement, a minimum normalized RSSI measurement, a mean of normalized RSSI measurements, a median of normalized RSSI measurements and a standard deviation of normalized RSSI measurements. The method further includes the step of (iv) training, a classifier of the system with a pre-configured value, using the first set of extracted features to obtain training data.

[0006] In an embodiment, the method may further comprise receiving a third set and a fourth set of RSSI measurements from a plurality of BT-LE beacons transmitted by a client device (e.g., a mobile communication device or any computing device that is capable of emitting or transmitting RSSI measurements) associated with a user; and performing the steps (ii) till (iii) to obtain a second set of extracted features, comparing the second set of extracted features with the training data, estimating, based on the comparison of the second set of extracted features with the training data, a distance of the user from each of the plurality of beacons, and localizing the user in the region of interest pertaining to the location based on the estimated distance.

[0007] In an embodiment, the method may further include determining an estimated distance of one or more users from a plurality of beacons by obtaining subsequent sets of RSSI measurements and normalizing the subsequent set of RSSI measurements, and localizing, using a mode of the normalized RSSI measurements, or a median of the normalized RSSI measurements, the one or more users based on the determined estimated distance.

[0008] In another aspect, a system is provided. The system comprising: a memory storing instructions, one or more communication interfaces, one or more hardware processors coupled to the memory through the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: (i) receive a first set and a second set of Received Signal Strength Indicator (RSSI) measurements from a plurality of Bluetooth low energy (BT-LE) beacons from a region of interest pertaining to a location having a first predetermined distance range, wherein each beacon from the plurality of beacons is positioned in the region of interest such that each beacon is separated from another beacon by a predetermined distance, (ii) normalize, for a second predetermined distance range, the first set and the second set of Received Signal Strength Indicator (RSSI) measurements received from the plurality of beacons to obtain a maximum tolerance based RSSI measurement and a minimum tolerance based RSSI measurement, (iii) extract, using the maximum tolerance based RSSI measurement and the minimum tolerance based RSSI measurement, one or more features from a first subset present in the set of normalized RSSI measurements by applying a slide bulging window technique on the first subset, and repeating the step (iii) until a last subset is reached in the second predetermined distance range to obtain a first set of extracted features. In an embodiment, the first set of extracted features comprise at least one of a maximum normalized RSSI measurement, a minimum normalized RSSI measurement, a mean of normalized RSSI measurements, a median of normalized RSSI measurements and a standard deviation of normalized RSSI measurements. The one or more hardware processors are further configured to (iv) train a classifier of the system with a pre-configured value, using the first set of extracted features to obtain training data.

[0009] In an embodiment, the one or more hardware processors are further configured to receive a third set and a fourth set of RSSI measurements from a plurality of BT-LE beacons transmitted by a client device associated with a user, and perform the steps (ii) till (iii) to obtain a second set of extracted features, perform a comparison the second set of extracted features with the training data, estimate, based on the comparison of the second set of extracted features with the training data, a distance of the user from each of the plurality of beacons, and localize the user in the region of interest pertaining to the location using the estimated distance.

[0010] In an embodiment, the one or more hardware processors are further configured to determine an estimated distance of one or more users from a plurality of beacons by obtaining subsequent sets of RSSI measurements and normalizing the subsequent set of RSSI measurements, and localize, using a mode of the normalized RSSI measurements, or a median of the normalized RSSI measurements, the one or more users based on the determined estimated distance.

[0011] It should be appreciated by those skilled in the art that any block diagram herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computing device or processor, whether or not such computing device or processor is explicitly shown.

BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:

[0013] FIG. 1 is a block diagram of a system for distance estimation and localization of users according to an embodiment of the present disclosure;

[0014] FIG. 2 is a flow diagram illustrating a method for distance estimation and localization of users using the system of FIG. 1 according to an embodiment of the present disclosure; and

[0015] FIG. 3 is a graphical representation illustrating error probability with respect to distance according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[0016] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0017] The words “comprising,” “having,” “containing,” and “including,” and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items.

[0018] It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the preferred, systems and methods are now described.

[0019] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.

[0020] Before setting forth the detailed explanation, it is noted that all of the discussion below, regardless of the particular implementation being described, is exemplary in nature, rather than limiting.

[0021] Referring now to the drawings, and more particularly to FIGS. 1 through 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.

[0022] FIG. 1 is a block diagram of a system 100 for distance estimation and localization of users according to an embodiment of the present disclosure. The system 100 comprises a memory 102, a hardware processor 104, and an input/output (I/O) interface 106. Although the exemplary block diagram and the associated description refers to a memory and a hardware processor, it may be understood that one or more memory units and one or more hardware processors may be comprised in the system 100. The memory 102 further includes one or more functional modules 108. The memory 102, the hardware processor 104, the input/output (I/O) interface 106, and/or the modules 108 may be coupled by a system bus or a similar mechanism.

[0023] The memory 102, may store instructions, any number of pieces of information, and data, used by a computer system, for example the system 100 to implement the functions of the system 100. The memory 102 may include for example, volatile memory and/or non-volatile memory. Examples of volatile memory may include, but are not limited to volatile random access memory (RAM). The non-volatile memory may additionally or alternatively comprise an electrically erasable programmable read only memory (EEPROM), flash memory, hard drive, or the like. Some examples of the volatile memory includes, but are not limited to, random access memory, dynamic random access memory, static random access memory, and the like. Some example of the non-volatile memory includes, but are not limited to, hard disks, magnetic tapes, optical disks, programmable read only memory, erasable programmable read only memory, electrically erasable programmable read only memory, flash memory, and the like. The memory 102 may be configured to store information, data, instructions or the like for enabling the system 100 to carry out various functions in accordance with various example embodiments.

[0024] Additionally or alternatively, the memory 102 may be configured to store instructions which when executed by the hardware processor 104 causes the system 100 to behave in a manner as described in various embodiments. The memory 102 stores the functional modules and information, for example, information (e.g., a first set and a second set of Received Signal Strength Indicator (RSSI) measurements) received from one or more beacons through the one or more networks (not shown in FIG. 1).

[0025] The hardware processor 104 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Further, the hardware processor 104 may comprise a multi-core architecture. Among other capabilities, the hardware processor 104 is configured to fetch and execute computer-readable instructions or modules stored in the memory 102. The hardware processor 104 may include circuitry implementing, among others, audio and logic functions associated with the communication. For example, the hardware processor 104 may include, but are not limited to, one or more digital signal processors (DSPs), one or more microprocessor, one or more special-purpose computer chips, one or more field-programmable gate arrays (FPGAs), one or more application-specific integrated circuits (ASICs), one or more computer(s), various analog to digital converters, digital to analog converters, and/or other support circuits.

[0026] The hardware processor 104 thus may also include the functionality to encode messages and/or data or information. The hardware processor 104 may include, among others a clock, an arithmetic logic unit (ALU) and logic gates configured to support operation of the hardware processor 104. Further, the hardware processor 104 may include functionality to execute one or more software programs, which may be stored in the memory 102 or otherwise accessible to the hardware processor 104.

[0027] FIG. 2, with reference to FIG. 1, is a flow diagram illustrating a method for distance estimation and localization of users using the system 100 according to an embodiment of the present disclosure. The steps of the method of the present disclosure will now be explained with reference to the components of the system 100 as depicted in FIG. 1. The system 100 is configured by the instructions stored in the memory 102. The system 100 (or the hardware processor 104) when configured by the instructions estimates distance and localizes users as described hereinafter. At step 202, the hardware processor 104 (i) receives a first set and a second set of Received Signal Strength Indicator (RSSI) measurements from a plurality of Bluetooth low energy (BT-LE) beacons from a region of interest pertaining to a location having a first predetermined distance range. Each beacon from the plurality of beacons is positioned in the region of interest such that each beacon is separated from another beacon by a predetermined distance. In an embodiment, the first set of RSSI measurements comprise ‘n’ line of sight RSSI measurements, and the second set of RSSI measurements comprise ‘n’ non-line of sight RSSI measurements, or vice-versa. Two points (RSSI measurements or beacons) A and B are said to be in line of sight as in ‘A’ is in line of sight of ‘B’ if and only if there exists a straight like ‘L’ that joins A and B, and does not pass through any of the constraints say C1, C2, ... Cn, wherein each constraint can be a curve. More particularly, the hardware processor 104 collects ‘n’ line of sight and ‘n’ non-line of sight RSSI measurements for each beacon in an environment, for distances from x to y meters at 1 meter interval wherein value of x is 1 and value of y is 8. Therefore the first set and the second set comprises RSSI measurements that are equal in number (or count). The first set and the second set of RSSI measurements may be received (or derived) from the plurality of beacons of a client device, in one example embodiment. In other words, the first set and the second set of RSSI measurements may be extracted upon receiving a plurality of beacons signals transmitted by the plurality of beacons from the client device.

[0028] At step 204, the hardware processor 104 normalizes, for a second predetermined distance range, the first set and the second set of Received Signal Strength Indicator (RSSI) measurements received from the plurality of beacons to obtain a set of normalized RSSI measurements, a maximum tolerance based RSSI measurement and a minimum tolerance based RSSI measurement. In an embodiment, a global maximum RSSI measurement and a global minimum RSSI measurement are calculated from the first set and the second set of RSSI measurements obtained from the plurality of beacons for a distance range of ‘a’ to ‘b’ meters, wherein value of ‘a’ is 1 and value of ‘b’ is 12 in an example embodiment. On top of that, a tolerance ‘t’ is added to obtain the maximum tolerance based RSSI measurement and the minimum tolerance based RSSI measurement. For example a RSSI measurement is considered as a data point, and data point is derived as <- (data point –min)/(max – min). Upon adding a tolerance, the maximum tolerance based RSSI measurement and the minimum tolerance based RSSI measurement are obtained by way of example expression illustrated below:

[0029] max <- max + t and min <- min – t

[0030] At step 206, the hardware processor 104 extracts one or more features from a first subset present in the set of normalized RSSI measurements by applying one or more slide bulging window techniques on the first subset using the maximum tolerance based RSSI measurement and the minimum tolerance based RSSI measurement. The hardware processor 104 repeats this step of applying one or more slide bulging window techniques on subsequent subsets and extracting features from subsets until a last subset is reached in the second predetermined distance range to obtain a set of extracted features (e.g., a first set of extracted features). For example, a slide-bulging window technique is used and is applied on the first ‘p’ samples of RSSI for feature extraction, and then next ‘q’ samples are added, so window size becomes p, p+q, p+2q etc., till 2n is reached for a given distance and the first set of extracted features are obtained. Therefore for each subset, one or more features are extracted that comprises of, but are not limited to, at least one of a maximum normalized RSSI measurement, a minimum normalized RSSI measurement, a mean of the normalized RSSI measurements, a median of the normalized RSSI measurements and a standard deviation of the normalized RSSI measurements. At step 208, the hardware processor 104 implements (or executes) a classifier (e.g., K-nearest neighbors classifier - a supervised machine learning technique) of the system 100. In an embodiment, the classifier may be stored in the memory 102 and executed. At this step 208, the hardware processor 104 trains, the classifier with a pre-configured value (e.g., 5), using the first set of extracted features to obtain training data.

[0031] The method may further include receiving, by the hardware processor 104 (or by the classifier), a third set and a fourth set of RSSI measurements from a plurality of BT-LE beacons transmitted by a client device associated with a user. In an embodiment, the third set and the fourth set may be less than or equal to in number (or count) as compared with the first set and the second set of RSSI measurements. In an embodiment, the third set of RSSI measurements may comprise ‘m’ line of sight RSSI measurements, and the fourth set of RSSI measurements may comprise ‘m’ non-line of sight RSSI measurements or vice-versa. The hardware process 104 (or the classifier) performs the steps 204 till 206 to obtain another set of extracted features (e.g., a second set of extracted features). The second set of extracted features comprise, but are not limited to, at least one of a maximum normalized RSSI measurement, a minimum normalized RSSI measurement, a mean of the normalized RSSI measurements, a median of the normalized RSSI measurements and a standard deviation of the normalized RSSI measurements. The system 100 (or the classifier) then performs a comparison of the second set of extracted features with the training data. Based on the comparison, the system 100 (or the classifier) estimates a distance of the user from each of the plurality of beacons, and accordingly localizes the user in the region of interest pertaining to the location based on the estimated distance. Based on the pattern of distance estimation and localization of users, the system 100 (or the classifier) determines an estimated distance of one or more users from a plurality of beacons by obtaining subsequent sets of RSSI measurements and normalizes the subsequent set of RSSI measurements; and further localizes the one or more users based on the determined estimated distance, using a mode of the subsequent normalized RSSI measurements, or a median of the subsequent normalized RSSI measurements. In other words, if output of the classifier contains repetition, the mode of the subsequent normalized RSSI measurements is taken to determine an estimated distance, and thus localize users; else the median value of the subsequent normalized RSSI measurements is taken for localization of users (e.g., for trilateration of users). A mode is defined as the entry in a set that repeats the maximum number of times. For example for the set {1, 2, 3, 3, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 7}, the mode is 5. Median is defined as a mid-value of the entries in the set (RSSI measurements).

[0032] FIG. 3, with reference to FIGS. 1-2, is a graphical representation illustrating error probability with respect to distance according to an embodiment of the present disclosure. More particularly, FIG. 3 depicts computation of error (by the system 100 or the classifier) that is marginal as the distance of users increases from one or more BL-TE beacons. Error measure is depicted along y-axis and distance (in meters) is depicted along x-axis in the graphical representation of FIG. 3. Below are results that have been experimented and tested.

[0033] Out of provided 30 points for each distance, 20 were used for training (bucket of 5). The rest 10 were used for testing (again in buckets of 5). Therefore for each distance, two estimates for the dataset were obtained. Results are provided as estimations, absolute error and finally mean +/- standard deviation. Reported accuracy for distance up to 8 meters as depicted in FIG. 3.
Beacon 1: Distance = 1.0 Estimate = 1.0
Beacon 1: Distance = 1.0 Estimate = 1.0
Beacon 1: Distance = 1.0 Estimate = 1.0
Beacon 1: Distance = 1.0 Estimate = 1.0
Beacon 1: Distance = 1.0 Estimate = 1.0
Beacon 1: Distance = 1.0 Estimate = 1.0
Beacon 1: Distance = 2.0 Estimate = 2.0
Beacon 1: Distance = 2.0 Estimate = 2.0
Beacon 1: Distance = 2.0 Estimate = 2.0
Beacon 1: Distance = 2.0 Estimate = 2.0
Beacon 1: Distance = 2.0 Estimate = 2.0
Beacon 1: Distance = 2.0 Estimate = 2.0
Beacon 1: Distance = 3.0 Estimate = 2.0
Beacon 1: Distance = 3.0 Estimate = 2.0
Beacon 1: Distance = 3.0 Estimate = 2.0
Beacon 1: Distance = 3.0 Estimate = 2.0
Beacon 1: Distance = 3.0 Estimate = 2.0
Beacon 1: Distance = 3.0 Estimate = 2.0
Beacon 1: Distance = 4.0 Estimate = 8.0
Beacon 1: Distance = 4.0 Estimate = 6.0
Beacon 1: Distance = 4.0 Estimate = 2.0
Beacon 1: Distance = 4.0 Estimate = 2.0
Beacon 1: Distance = 4.0 Estimate = 2.0
Beacon 1: Distance = 4.0 Estimate = 2.0
Beacon 1: Distance = 5.0 Estimate = 2.0
Beacon 1: Distance = 5.0 Estimate = 2.0
Beacon 1: Distance = 5.0 Estimate = 8.0
Beacon 1: Distance = 5.0 Estimate = 8.0
Beacon 1: Distance = 5.0 Estimate = 8.0
Beacon 1: Distance = 5.0 Estimate = 8.0
Beacon 1: Distance = 6.0 Estimate = 6.0
Beacon 1: Distance = 6.0 Estimate = 6.0
Beacon 1: Distance = 6.0 Estimate = 8.0
Beacon 1: Distance = 6.0 Estimate = 8.0
Beacon 1: Distance = 6.0 Estimate = 8.0
Beacon 1: Distance = 6.0 Estimate = 8.0
Beacon 1: Distance = 7.0 Estimate = 2.0
Beacon 1: Distance = 7.0 Estimate = 2.0
Beacon 1: Distance = 7.0 Estimate = 2.0
Beacon 1: Distance = 7.0 Estimate = 5.0
Beacon 1: Distance = 7.0 Estimate = 4.0
Beacon 1: Distance = 7.0 Estimate = 4.0
Beacon 1: Distance = 8.0 Estimate = 2.0
Beacon 1: Distance = 8.0 Estimate = 2.0
Beacon 1: Distance = 8.0 Estimate = 2.0
Beacon 1: Distance = 8.0 Estimate = 2.0
Beacon 1: Distance = 8.0 Estimate = 8.0
Beacon 1: Distance = 8.0 Estimate = 8.0
Beacon 2: Distance = 1.0 Estimate = 2.0
Beacon 2: Distance = 1.0 Estimate = 2.0
Beacon 2: Distance = 1.0 Estimate = 2.0
Beacon 2: Distance = 1.0 Estimate = 2.0
Beacon 2: Distance = 1.0 Estimate = 2.0
Beacon 2: Distance = 1.0 Estimate = 2.0
Beacon 2: Distance = 2.0 Estimate = 2.0
Beacon 2: Distance = 2.0 Estimate = 2.0
Beacon 2: Distance = 2.0 Estimate = 2.0
Beacon 2: Distance = 2.0 Estimate = 2.0
Beacon 2: Distance = 2.0 Estimate = 3.0
Beacon 2: Distance = 2.0 Estimate = 5.0
Beacon 2: Distance = 3.0 Estimate = 1.0
Beacon 2: Distance = 3.0 Estimate = 1.0
Beacon 2: Distance = 3.0 Estimate = 2.0
Beacon 2: Distance = 3.0 Estimate = 2.0
Beacon 2: Distance = 3.0 Estimate = 6.0
Beacon 2: Distance = 3.0 Estimate = 6.0
Beacon 2: Distance = 4.0 Estimate = 1.0
Beacon 2: Distance = 4.0 Estimate = 1.0
Beacon 2: Distance = 4.0 Estimate = 3.0
Beacon 2: Distance = 4.0 Estimate = 3.0
Beacon 2: Distance = 4.0 Estimate = 3.0
Beacon 2: Distance = 4.0 Estimate = 3.0
Beacon 2: Distance = 5.0 Estimate = 2.0
Beacon 2: Distance = 5.0 Estimate = 2.0
Beacon 2: Distance = 5.0 Estimate = 2.0
Beacon 2: Distance = 5.0 Estimate = 2.0
Beacon 2: Distance = 5.0 Estimate = 2.0
Beacon 2: Distance = 5.0 Estimate = 2.0
Beacon 2: Distance = 6.0 Estimate = 7.0
Beacon 2: Distance = 6.0 Estimate = 7.0
Beacon 2: Distance = 6.0 Estimate = 8.0
Beacon 2: Distance = 6.0 Estimate = 8.0
Beacon 2: Distance = 6.0 Estimate = 8.0
Beacon 2: Distance = 6.0 Estimate = 8.0
Beacon 2: Distance = 7.0 Estimate = 6.0
Beacon 2: Distance = 7.0 Estimate = 7.0
Beacon 2: Distance = 7.0 Estimate = 7.0
Beacon 2: Distance = 7.0 Estimate = 7.0
Beacon 2: Distance = 7.0 Estimate = 7.0
Beacon 2: Distance = 7.0 Estimate = 7.0
Beacon 2: Distance = 8.0 Estimate = 8.0
Beacon 2: Distance = 8.0 Estimate = 8.0
Beacon 2: Distance = 8.0 Estimate = 8.0
Beacon 2: Distance = 8.0 Estimate = 8.0
Beacon 2: Distance = 8.0 Estimate = 8.0
Beacon 2: Distance = 8.0 Estimate = 8.0

[0034] Distance Errors: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 0.0, 0.0, 2.0, 2.0, 2.0, 2.0, 5.0, 5.0, 5.0, 2.0, 3.0, 3.0, 6.0, 6.0, 6.0, 6.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 2.0, 2.0, 1.0, 1.0, 3.0, 3.0, 3.0, 3.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0].

[0035] Error Measure: 1.60416666667 +/- 1.59738827639

[0036] The distance errors may be calculated by way of the following illustrative expression:
Error = Abs(actual – estimated)

[0037] In other words, a distance error may be measured based on an actual distance value and an estimated distance value. More particularly, the distance error is an absolute value of the difference between an actual distance value and an estimated distance value.

[0038] The error measure may be computed (or calculated) based on the following illustrative expression:
Error Measure = Mean +/- Standard deviation of the error vector across distances.
[0039] The embodiments of the present disclosure enable the system 100 and the method to estimate distance of users from the BT-LE beacons and thereby localize the users in a given environment. The embodiments of the present disclosure enable the system 100 extract statistical features of RSSI measurements from each beacon to derive at a beacon specific model which considers both the beacon to beacon variation and also the environment in which it is placed. This model provides distance estimates, at meter level, which is then used for trilateration (or localizing of users) for a given location/environment. The embodiments of the present disclosure further enable the system 100 to iterative perform least square based trilateration for obtaining or determining final position estimate of users from beacons. Unlike conventional systems and methods which use fingerprinting techniques, the proposed system 100 implements the classifier (e.g., the KNN classifier), to estimate distance where the error is marginal as compared to errors estimated by the conventional systems and methods. Further, unlike conventional systems and methods which do not account RSSI fluctuations of transmitters and receivers, since the system 100 uses the classifier and extraction of features technique for BT-LE beacons, and accounts for beacon to beacon differences, and environment to environment differences by considering line of sight RSSI measurements and non-line of sight RSSI measurements, the system 100 is able to estimate distance of users from beacons and thus localize users in a given location/environment more accurately and ensuring less prone to errors where the error is marginal as compared to the errors estimated by the conventional systems and methods.

[0040] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.

[0041] It is, however to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.

[0042] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0043] The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W), BLU-RAY and DVD.

[0044] A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.

[0045] Input/output (I/O) devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.

[0046] A representative hardware environment for practicing the embodiments may include a hardware configuration of an information handling/computer system in accordance with the embodiments herein. The system herein comprises at least one processor or central processing unit (CPU). The CPUs are interconnected via system bus to various devices such as a random access memory (RAM), read-only memory (ROM), and an input/output (I/O) adapter. The I/O adapter can connect to peripheral devices, such as disk units and tape drives, or other program storage devices that are readable by the system. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein.

[0047] The system further includes a user interface adapter that connects a keyboard, mouse, speaker, microphone, and/or other user interface devices such as a touch screen device (not shown) to the bus to gather user input. Additionally, a communication adapter connects the bus to a data processing network, and a display adapter connects the bus to a display device which may be embodied as an output device such as a monitor, printer, or transmitter, for example.

[0048] The preceding description has been presented with reference to various embodiments. Persons having ordinary skill in the art and technology to which this application pertains will appreciate that alterations and changes in the described structures and methods of operation can be practiced without meaningfully departing from the principle, spirit and scope.