Realization of enterprise-grade location identification using Wi-Fi 802.11mc fine-tuning measurement

Abstract

A method is presented for evaluating the feasibility of a WiFi Fine Tune Measurement (FTM) based ranging protocol for enterprise-grade location location with heterogeneity among operational parameters. The method includes enabling a plurality of access points to measure distances to a plurality of mobile devices handled by a plurality of users in an enterprise environment, tracking a mobile device of the plurality of mobile devices as it moves through the enterprise environment using an on-board sensor, measuring a plurality of distances to at least three of the plurality of access points by the mobile device at various points along the path, combining the plurality of distances with information related to the path to predict a ranging offset of the mobile device, and the mobile device self-calibrating all of its ranging offsets on demand.

Description

[Technical field]

[0001] The present invention relates to WiFi location solutions, and more specifically to achieving enterprise grade location using 802.11mc fine tuning measurements. [Background technology]

[0002] WiFi location solutions have been significantly improved with the introduction of the Fine Time Measurement (FTM) ranging protocol in the IEEE 802.11-2016 standard (i.e., the 802.11mc standard). The ability to integrate WiFi ranging (measurement of distance between two devices with FTM) from the user space of off-the-shelf devices has enabled third-party service providers to develop enterprise-grade solutions to locate both people and assets in large indoor spaces, leveraging pre-deployed infrastructure (e.g., WiFi access points (APs)) and end-user devices such as smartphones and tablets. Unlike other evolving radio frequency (RF) technologies such as Ultra-Wide Band (UWB), which also offer ranging capabilities, WiFi devices are ubiquitous, have greater coverage, and have better penetration (lower operating frequency and higher power than UWB), making WiFi FTM more convenient and practical for enterprise-grade indoor solutions. Furthermore, most modern devices already implement WiFi FTM. Summary of the Invention

[0003] A method is presented for evaluating the feasibility of a WiFi FTM (Fine Tune Measurement) based ranging protocol for enterprise-grade location location with heterogeneity among operational parameters, the method includes enabling a plurality of access points to measure distances to a plurality of mobile devices handled by a plurality of users in an enterprise environment, tracking a mobile device of the plurality of mobile devices as it moves through the enterprise environment using an on-board sensor, measuring a plurality of distances to at least three of the plurality of access points by the mobile device at various points along the path, combining the plurality of distances with information related to the path to predict a ranging offset of the mobile device, and the mobile device self-calibrating all of its ranging offsets on demand.

[0004] A non-transitory computer-readable medium is presented, including a computer-readable program for evaluating the feasibility of a WiFi FTM-based ranging protocol for enterprise-grade location location with heterogeneity among operational parameters, the computer-readable program, when executed on a computer, causes the computer to: enable a plurality of access points to measure distances to a plurality of mobile devices handled by a plurality of users in an enterprise environment; track a mobile device of the plurality of mobile devices as it moves through the enterprise environment using an on-board sensor; measure a plurality of distances by the mobile device to at least three of the plurality of access points at various points along a route; combine the plurality of distances with information related to the route to predict a ranging offset of the mobile device; and have the mobile device self-calibrate all of its ranging offsets on demand.

[0005] A system is presented for evaluating the feasibility of a WiFi Fine Tune Measurement (FTM) based ranging protocol for enterprise-grade location location with heterogeneity among operational parameters, the system includes a plurality of access points for measuring distances to a plurality of mobile devices handled by a plurality of users in an enterprise environment, the system tracks a mobile device of the plurality of mobile devices as it moves through the enterprise environment using an on-board sensor, the mobile device measures a plurality of distances to at least three of the plurality of access points at various points along a route, combines the plurality of distances with route-related information to predict a ranging offset for the mobile device, and the mobile device self-calibrates all of its ranging offsets on demand.

[0006] These and other features and advantages will become apparent from the following detailed description of exemplary embodiments, which is to be read in conjunction with the accompanying drawings.

[0007] In the present disclosure, preferred embodiments are described in detail with reference to the following drawings, as described below. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a block / flow diagram of an exemplary Fine Tune Measurement (FTM) ranging protocol message sequence.

[0009] [Diagram 2] FIG. 2 is a block / flow diagram of an exemplary FTM frame format.

[0010] [Diagram 3] FIG. 3 is a block / flow diagram of an exemplary WiLoc scenario, in accordance with an embodiment of the present invention.

[0011] [Figure 4]FIG. 4 illustrates an exemplary practical application for evaluating the feasibility of a WiFi FTM-based ranging protocol for enterprise-grade localization with heterogeneity among operational parameters, in accordance with an embodiment of the present invention.

[0012] [Diagram 5] FIG. 5 illustrates an exemplary processing system for evaluating the feasibility of a WiFi FTM-based ranging protocol for enterprise-grade location location with heterogeneity among operational parameters, in accordance with an embodiment of the present invention.

[0013] [Figure 6] FIG. 6 is a block / flow diagram of an exemplary method for evaluating the feasibility of a WiFi FTM-based ranging protocol for enterprise-grade positioning with heterogeneity among operational parameters, in accordance with an embodiment of the present invention.

[0014] [Figure 7] FIG. 7 is a block / flow diagram of an example equation for a linear solver, a quadratic solver and a ranging offset, in accordance with an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] The WiFi Consortium envisions a broader reach and ecosystem of location-aware WiFi services under the umbrella of WiFiAware networking. However, to date there are no convincing commercial solutions that leverage WiFi Fine Tune Measurement (FTM) for location. Therefore, it is unclear whether WiFi FTM is ready for prime-time location at enterprise scale.

[0016] Previous research has conducted experimental studies to understand WiFi FTM in indoor environments. WiFi FTM is exploratory in nature and therefore focuses primarily on the accuracy of distance measurements in limited experimental settings between homogeneous WiFi devices (using similar Intel WiFi Network Interface Cards (NICs)). Establishing whether FTM can provide a practical localization solution requires understanding the ability of its ranging primitives to seamlessly and accurately interoperate with heterogeneous devices before an "enterprise-grade" solution can be realized. To this end, the exemplary method presents one of the first comprehensive experimental studies focusing on heterogeneity in multiple dimensions (device, spectrum, environment) to answer this question.

[0017] Extensive measurement studies have been performed using multiple commercial equipment (e.g., six WiFi Access Points (APs) and three WiFi clients from different vendors) under different spectrum (ranging bandwidths and channels) and environmental (line of sight (LoS) vs. near line of sight (NLoS), cluttered vs. open indoor) conditions.

[0018] The main inferences from this study are as follows:

[0019] The problem with FTM ranging is that the fixed ranging offset (which is independent of the AP-client distance) can vary significantly for different AP-client pairs.

[0020] The ranging offset for a particular AP-client pair can vary significantly with different ranging channels (up to 3.6m) and channel bandwidths (up to 16.2m).

[0021] Ranging errors are significantly amplified in NLoS conditions. Moreover, unlike LoS conditions, wider channels do not necessarily improve ranging accuracy in NLoS conditions.

[0022] The presence of fixed offsets within the estimated distances has been observed before, but the reasons for this were unclear, and the offsets were addressed by prior calibration between fixed sets of equipment. However, this extensive study, conducted across multiple dimensions, sheds light on the magnitude and impact of these offsets, as well as their potential causes. To compensate for the effects of wireless multipath and direct path detection delays, equipment vendors were found to employ their own compensation algorithms. In fact, the standard allows room for vendors to advertise such compensation. Although common vendors of APs and clients can have compensation algorithms with synergy, it is not possible to handle and scale this across vendors, resulting in offsets ranging from -12m to 14m, with localization errors of up to 20m. In fact, these offsets cannot be considered and calibrated out of the box, which creates a fundamental problem in leveraging FTM for practical localization in the enterprise.

[0023] This points to the conclusion that WiFi FTM is not ready for primetime, enterprise-scale location.

[0024] However, despite these disappointing results, it is believed that the door to leveraging FTM is not completely closed. As a solution, the exemplary embodiment proposes a simple but effective over-the-top (OTT) auto-calibration solution that allows all WiFi devices to self-calibrate their offset when entering an enterprise environment, making WiFi FTM useful for location location. The exemplary approach follows the concept of simultaneous location and calibration, whereby it exploits the mobility of the client device to measure multiple distances to the AP at various points in the client's path, and in combination with its mobility (informed by the inertial sensors) to jointly solve the client's own location while correcting for the unknown offset. The solution can be easily deployed as an Android or iOS application on the client device, allowing for seamless integration.

[0025] The research results include an extensive measurement study of WiFi FTM, conducted to evaluate the practical feasibility of WiFi FTM in enterprise-grade localization, focusing on the heterogeneity among various operational parameters. Exemplary embodiments propose WiLoc, an OTT auto-calibration solution that solves the fundamental challenge in inaccurate distance estimation, and demonstrate that useful localization (average accuracy of about 2 m) can be achieved with WiFi FTM despite large ranging offsets.

[0026] This illustrative study is the first to investigate the shortcomings of WiFi FTM in a broader enterprise environment and discuss improvements towards a practical location solution.

[0027] The IEEE 802.11mc FTM ranging protocol allows two WiFi devices with asynchronous clocks to cooperatively estimate their distance from each other by measuring the round trip time (RTT). To measure the RTT, a station (initiator 110) and an AP (responder 120) exchange bursts of messages with each other, as shown at 100 in Figure 1.

[0028] Both STA110 and AP120 record the time-of-departure (ToD) of the sent FTM message and the received FTM message, e.g., T1(k) and T3(k) in FIG. 1, and the time-of-arrival (ToA), e.g., T2(k) and T4(k) in FIG. 1, respectively.

[0029] Then the RTT (for k bursts) is calculated as follows:

number

[0030] Once the RTT is calculated, the distance is calculated as D=1 / 2*RTT*C, where C is the speed of light.

[0031] The success of the FTM ranging protocol depends on the nodes being able to accurately record the ToA and ToD. However, in practice, implementations can introduce offsets by acquiring the ToD earlier or later than the signal arrives at the transmit antenna connector. Similarly, it can take a significant amount of time between the time the preamble arrives at the receive antenna (the actual ToA) and the time the node detects the frame, synchronizes with its logical structure and calculates the ToA. The standard requires that the device compensates for this delay in the calculation of the ToA by subtracting the offset from the calculated ToA. However, the standard does not provide guidelines on how exactly to determine this offset, so vendors end up implementing their own algorithms to compensate the ToA / ToD estimates, which often result in over- or under-correction, leading to longer or shorter distances. The problem is exacerbated when equipment from two different vendors interacts with each other, as each device's (inaccurate) ToA / ToD estimates contribute (disproportionately) to the final ranging offset.

[0032] In 802.11mc frames there are specific fields that allow devices (both STAs and APs) to indicate to ranging peers the potential errors in their ToA and ToD estimations (ToA Error and ToD Error fields, elements 210 and 220 in Figure 2). The standard provides this option to get an accurate picture of the reliability of the device in its offset predictions. However, measurement studies have shown that these options are not actually used by commercially available devices.

[0033] Interestingly, it was observed that the ranging offset of paired devices is constant for a particular combination of center frequency (channel) and channel bandwidth (e.g., 20 / 40 / 80MHz) used in the FTM protocol. It is believed that devices use a constant offset value depending on the channel and bandwidth used (to account for different processing overhead in wider channels) to compensate for inaccurate ToA / ToD estimation. To understand the scale of the ranging offset problem, a detailed measurement study was performed using several commercially available devices.

[0034] In the performed measurement study, six different WiFi APs (ASUS RTACRH13, Linksys Velop, Google Nest Mesh AP system (consisting of three APs in a mesh network, called Google1, Google2, and Google3), and Compulab Fitlet AP) and three different STAs (Google Pixel 5, Xiaomi Mi Note 10, and Compulab Fitlet acting as STA) are used. Each of these devices employs a different WiFi chipset, and all devices support channel widths up to 80 MHz. It is noted that some vendors (e.g. Google®) do not allow manual selection of channels and bandwidths. Therefore, for these devices, only the channel and bandwidth configuration selected by the device for FTM ranging is used.

[0035] For data collection, the iw utility in the Linux OS was modified using a patch to enable seamless FTM ranging of the Fitlet device. For Android smartphones, an app was created that uses the WiFi Ranging Android API to send FTM requests periodically (e.g., every 30 ms). Data is collected in both LoS and NLoS scenarios in a variety of indoor environments in an office building and a university campus: a large carpeted area, a lab with benches and equipment, a building lobby with furniture, and a large open space with tall glass windows.

[0036] For LoS conditions, we evaluated WiFi FTM ranging performance using static STAs in LoS scenarios. For each AP-STA pair, the static STA was placed at various distances from 1m to 50m and the FTM distance was recorded for all AP-STA pairs' supported channels and bandwidths (channels 36 / 80MHz). The actual distance between the client and AP with respect to the ground truth (GT) was physically measured. The GT distance was used to calculate the ranging offset. Two observations, i.e., there is a fixed ranging offset regardless of the distance between the AP and the STA, and the ranging offset varies significantly for different AP-client pairs, highlighting how the offset issue can vary significantly between different types of devices.

[0037] Regarding the impact of operating frequency, we evaluated how the ranging offsets change based on the operating channel (for fixed channel bandwidth). It was observed that the devices implement frequency dependent offsets (for ToA / ToD correction). However, the variation of these offsets for a given AP-client pair is not significant. The maximum offset difference between different channels observed with a Xiaomi client and an ASUS AP is 3.6m.

[0038] Regarding the effect of channel bandwidth, we show how the ranging offset changes with different channel bandwidths (same center frequency) for a given AP-STA pair. The ranging offset for the same device pair can vary up to 16.2m (for ASUS AP-Fitlet client) or as low as 1.2m (for Fitlet AP-Pixel5 client).

[0039] Regarding the accuracy of FTM ranging with offset correction, the accuracy of FTM ranging with (manual) offset correction was evaluated. For previously collected data, the offset is estimated as the average difference between the measured distance and the GT distance, which is used as the offset value. This offset is subtracted from the measured distance to obtain the distance error. Across pairs of devices, the distance errors are found to be about 50 cm, about 30 cm, and about 10 cm, respectively, for the median case, suggesting that once the different ranging offsets are resolved, FTM ranging in LoS is very accurate and useful for precise localization.

[0040] We evaluated the FTM ranging accuracy in NLoS conditions with respect to NLoS conditions. To determine the FTM ranging error, we manually discount the ranging offset, which is calculated from the LoS measurements, the measured distance, and the accuracy of the measured distance. Unlike LoS, the ranging error (for a given channel and bandwidth) is amplified in NLoS conditions. The combination of the poor channel quality and the device's inability to accurately identify the peaks of the channel impulse response (a common problem with multipath signals) further degrades the ToA estimation, resulting in large ranging errors. The 80 MHz channel bandwidth, which allows the most accurate distance estimation in LoS, is most affected in NLoS because signals using a wider bandwidth (e.g., 80 MHz) attenuate faster than the same signal spread across a narrower bandwidth (e.g., 20 MHz). More importantly, for location solutions implementing FTM ranging, we found that wider channels do not necessarily yield more accurate distances.

[0041] Regarding the impact on LoS vs. NLoS FTM ranging and localization, given the high inaccuracy of NLoS range estimation, a localization system using FTM ranging needs to be able to distinguish between LoS and NLoS distances and to exclude NLoS distances.

[0042] To perform this classification, a simple machine learning model such as Random Forest is employed, using three inputs: the last n distances, the last n RSSI values, and the variance of the last n distances. In an exemplary method, the ranging attempts to accurately classify whether the AP is in LoS or NLoS. Even using only five distance samples taken every 30 ms, the model is able to correctly classify LoS and NLoS distances in 83.5% of cases. Furthermore, assuming that the user stays in LoS / NLoS for a long time, taking more distance / RSSI samples at more intervals further increases the accuracy, up to 99.5%, considering the previous 20 distance / RSSI values ​​taken per second.

[0043] In summary, we found that the ranging offset depends on the AP-STA pair, the channel width and the channel (frequency). Since there are various vendors of WiFi equipment and the spectrum configuration of the equipment changes frequently, it becomes virtually impossible to provide an offline solution to this problem. The only way to address this challenge is to use an on-demand, real-time, online solution.

[0044] Furthermore, when a user moves from a LoS state to a NLoS state, the ranging accuracy degrades significantly. Therefore, it becomes important to distinguish between LoS distance and NLoS distance and design a system that can use only LoS distance while excluding NLoS distance.

[0045] To solve the ranging offset problem in a practical way, an exemplary method proposes WiLoc, a novel over-the-top (OTT) solution that can be deployed as an application on end-user devices such as smartphones, allowing these devices to self-calibrate ranging offsets at multiple APs without human input when they enter an enterprise environment. Broadly speaking, WiLoc follows the concept of simultaneous localization and calibration. WiLoc employs Euclidean geometry that leverages the user's mobility, calculated with on-board inertial sensors, to measure distances at various points of the path, and combines both path and distance information to solve the ranging offset.

[0046] WiLoc300 is AP310 (x AP ,y AP ) (Figure 3), and assume that the user starts from (xc, yc) and moves continuously in a random direction relative to the AP 310. At a time interval t (e.g., t = 1 second), the STA 320 performs FTM ranging and obtains the distance R P Measure where R P =r P +δ, where δ is the ranging offset. To solve for δ, WiLoc 300 lin and WiLoc quad The two approaches are: lin uses a system of linear equations to solve for δ, whereas WiLoc quad employs a quadratic solver. A linear approach is less complicated but produces a less accurate estimate of δ, whereas a quadratic approach is more complicated but produces a more accurate estimate.

[0047] The first approach, WiLoc lin (854 in Figure 4) formulates the problem of solving δ using a set of linear equations. lin is the position of the AP (x AP ,y AP )=(0,0).

[0048] The initial position (xc, yc) of STA is

number

[0049] As the user moves, in each time interval t, the STA 320 arrives at a new position P, which is given by the additional equation:

number

[0050] Here, α i is calculated by measuring the azimuth (magnetometer) information, and d i (Movement) is estimated using the STA's inertial sensors (accelerometer, magnetometer).

[0051] An exemplary method subtracts the value of equation (1) from the value of every new equation (2) obtained at each time interval t. This linearizes a system of equations with three unknown variables (xc, yc, and δ). At least three equations (3 seconds for t=1) are needed to solve for three unknown variables, and adding additional equations improves the accuracy of the solution (δ).

[0052] The second approach, WiLoc quad (856 in Figure 4) employs a quadratic solver.

[0053] WiLoc quad Assume that the initial position of the STA is (xc, yc) = (0, 0), and the AP is (x AP ,y AP ), the Euclidean distance between the AP and the STA is expressed as follows:

number

[0054] At each time instant t, if the user is at position P, the new Euclidean distance is:

number

[0055] An exemplary method is to subtract the value of equation (3) from each new value of equation (4) at each time interval t (i.e., (4)-(3)). This cancels δ and leaves the two unknown variables, namely x AP andy AP WiLoc quad In , only two equations are needed to solve for the unknown variables, and each additional equation improves the accuracy of the solution.

[0056] WiLoc quad x AP andy AP Solving for, the ranging offset δ is given by:

number

[0057] Here, R1 is the distance when the user (STA) was at (0,0).

[0058] WiLoc300 schedules a STA to measure distances to multiple APs (at least three) in sequence during each time interval t, and calculates δ AP to calculate the actual distance from each AP. The LoS APs are selected by a random forest classifier. WiLoc300 then locates the STA using a simple least-squares multilateration solver, with the offset-corrected distance from the LoS APs as input to the solver.

[0059] In conclusion, WiFi's fine tune measurement (FTM) based ranging protocol has set the stage for mass adoption of location-aware applications and services in WiFi-enabled enterprise and consumer ecosystems. However, the lack of deployment of such commercial-scale location solutions has prompted a comprehensive experimental study aimed at validating whether WiFi's FTM is truly ready for prime-time location.

[0060] As operational heterogeneity (equipment, environment, spectrum) is the fundamental nature of commercial deployments, the exemplary study focuses on FTM's ability to achieve usable localization under such practical conditions. This exemplary study is the first of its kind and reveals several interesting insights into the real-world operation of FTM, the most interesting of which is the inability to eliminate significant offsets in the estimated distances between heterogeneous devices and configurations, resulting in a significant degradation in performance (up to 20m error). Although a disappointing result for the readiness of FTM, the exemplary method also proposes a simple but promising solution: an over-the-top auto-calibration solution that allows all WiFi devices entering the enterprise environment to self-calibrate their offsets on demand, making the use of FTM more useful for localization (average error of 2m).

[0061] FIG. 4 is a block / flow diagram 800 of a practical application for evaluating the feasibility of a WiFi Fine Tune Measurement (FTM) based ranging protocol for enterprise-grade positioning with heterogeneity among operational parameters in accordance with an embodiment of the present invention.

[0062] In one practical example, a transmitter 802 is in communication with a receiver 804, and WiLoc 300 lin (854) or WiLoc quadThe results 810 (e.g., variables, parameters, factors, or communications) may be provided or displayed in a user interface 812 for interaction with a user 814.

[0063] With regard to practical applications of the exemplary embodiments, the novelty of 5G is the integration of multiple networks serving different sectors, domains and applications such as multimedia, virtual reality (VR) and augmented reality (AR), machine to machine (M2M) and internet of things (IoT), automotive applications, smart cities, etc. The diversity of 5G applications and the associated service requirements in terms of data rates, latency, reliability and other parameters will force operators to offer a variety of 5G networks.

[0064] Among the various technological innovations enabling 5G, one of the main needs and enablements is the use of mmWave spectrum combined with network densification and massive multiple input multiple output (MIMO) to act as an ultra-high speed access and backhaul system. A key aspect of 5G is enabling applications in the mmWave spectrum such as mobile edge computing (MEC), which is expected to bring information and processing closer to mobile users, enabling ultra-high speed and low latency communications. Exemplary embodiments of the present invention enable such applications to be successfully implemented.

[0065] FIG. 5 is an exemplary processing system for evaluating the feasibility of a WiFi Fine Tune Measurement (FTM) based ranging protocol for enterprise-grade positioning with heterogeneity among operational parameters, in accordance with an embodiment of the present invention.

[0066] The processing system includes at least one processor (CPU) 904 operatively connected to other components via a system bus 902. Also operatively connected to the system bus 902 are a GPU 905, a cache 906, a read-only memory (ROM) 908, a random access memory (RAM) 910, an input / output (I / O) adapter 920, a network adapter 930, a user interface adapter 940, and / or a display adapter 950. Additionally, the app slice 950 includes an application slice specification 952 and a function slice specification 954. Additionally, the WiLOC 300 includes a linear solver 854 (WiLoc lin ) or Quadratic Solver 856 (WiLoc quad ) can be used.

[0067] The storage devices 922 are operably connected to the system bus 902 by the I / O adapter 920. The storage devices 922 may be any of a disk storage device (e.g., a magnetic disk storage device or an optical disk storage device), a solid state magnetic device, or the like.

[0068] The transceiver 932 is operably connected to the system bus 902 by the network adapter 930 .

[0069] User input device(s) 942 are operatively connected to system bus 902 by user interface adapter 940. User input device 942 may be any of a keyboard, mouse, keypad, image capture device, motion sensing device, microphone, or a device incorporating the functionality of at least two of these devices. Of course, other types of input devices may be used while maintaining the spirit of the principles of the present invention. Of course, other types of input devices may be used while maintaining the spirit of the present invention. User input device(s) 942 may be the same type of user input device or may be different types of user input devices. User input device 942 is used to input information to and output information from the processing system.

[0070] A display device 952 is operatively connected to the system bus 902 by a display adapter 950 .

[0071] Of course, the processing system may include other elements (not shown) or omit certain elements, as would be readily apparent to one of ordinary skill in the art. For example, the processing system may include various other types of input and / or output devices, depending on the particular implementation, as would be readily apparent to one of ordinary skill in the art. For example, various wireless and / or wired input and / or output devices may be used. Furthermore, various configurations of additional processors, controllers, memory, etc. may be used, as would be readily apparent to one of ordinary skill in the art. These and other variations of the processing system will be readily apparent to one of ordinary skill in the art from the teachings of the present principles provided herein.

[0072] FIG. 6 is a block / flow diagram of an exemplary method for evaluating the feasibility of a WiFi Fine Tune Measurement (FTM) based ranging protocol for enterprise-grade positioning with heterogeneity among operational parameters in accordance with an embodiment of the present invention.

[0073] At block 1001, an access point is enabled to communicate with multiple mobile devices operated by multiple users within an enterprise environment.

[0074] At block 1003, a mobile device of a plurality of mobile devices is tracked as it moves along a route in an enterprise environment.

[0075] At block 1005, multiple distance measurements are taken at various points along the path.

[0076] In block 1007, the multiple distance and route related information are combined to predict a ranging offset for the mobile device, enabling the mobile device to self-calibrate all of its ranging offsets on demand.

[0077] FIG. 7 is a block / flow diagram of example equations for a linear solver, a quadratic solver and a ranging offset, in accordance with an embodiment of the present invention.

[0078] WiLOC300 is a linear solver 854 (WiLoc lin ) or Quadratic Solver 856 (WiLoc quad ) can be implemented using the quadratic solver 856 (WiLoc quad ) The ranging offset equation is given by 1111.

[0079] As used herein, the terms "data," "content," "information," and similar terms may be used interchangeably to refer to data that may be obtained, transmitted, received, displayed, and / or stored by various exemplary embodiments. Thus, the use of these terms should not be construed as limiting the spirit and scope of the disclosure. Additionally, where a computing device is described herein for receiving data from another computing device, the data may be received directly from the other computing device or indirectly via one or more intermediate computing devices, such as one or more servers, relays, routers, network access points, base stations, and the like. Similarly, where a computing device is described herein for transmitting data to another computing device, the data may be transmitted directly to the other computing device or indirectly via one or more intermediate computing devices, such as one or more servers, relays, routers, network access points, base stations, and / or the like.

[0080] As will be appreciated by those skilled in the art, aspects of the present invention may be embodied as a system, method, or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, which may be generally referred to herein as a "circuit," "module," "computer," "apparatus," or "system." Additionally, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code thereon.

[0081] Any combination of one or more computer readable media may be used. The computer readable medium may be a computer readable signal medium or a computer readable recording medium. The computer readable recording medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, apparatus or device, or any suitable combination of the foregoing. More specific examples of computer readable recording media, including but not limited to, include one or more wires, portable computer diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), optical fibers, portable compact disc read-only memories (CD-ROMs), optical data storage devices, magnetic data storage devices, or any suitable combination of the foregoing. In the context of this document, a computer readable recording medium may be any tangible medium that contains or can store a program for use by or in connection with an instruction execution system, apparatus or device.

[0082] A computer-readable signal medium may include a propagated data signal in which computer-readable program code is embodied, for example in baseband or as part of a carrier wave. Such a propagated signal may be in any of a variety of forms, including but not limited to electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium is not a computer-readable recording medium, but may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, device, or apparatus.

[0083] The program code embodied in the computer readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

[0084] Computer program code for carrying out processes related to aspects of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as the "C" programming language or similar programming languages. The program code may run entirely on the user's computer, partially on the user's computer as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet Service Provider).

[0085] Aspects of the present invention are described below with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions are provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to generate a machine such that the instructions, executed by a processor of the computer or other programmable data processing apparatus, create means for performing the functions / acts specified in one or more blocks or modules of the flowcharts and / or block diagrams.

[0086] These computer program instructions can be stored on a computer-readable medium that can instruct a computer, other programmable data processing device, or other device to function in a particular manner, such that the instructions stored on the computer-readable medium generate a product including instructions for implementing the functions / acts specified in one or more blocks or modules of the flowcharts and / or block diagrams.

[0087] Computer program instructions can be loaded into a computer, other programmable data processing apparatus or other device to generate a computer-implemented process such that a series of operational steps are executed on the computer, other programmable apparatus or other device, and the instructions executing on the computer or other programmable apparatus provide a process for implementing the functions / operations specified in the blocks or modules of the flowcharts and / or block diagrams.

[0088] It should be noted that the term "processor" as used herein is intended to include any processing device, such as, for example, one that includes a central processing unit (CPU) and / or other processing circuitry. It should also be understood that the term "processor" may refer to one or more processing devices, and that various elements associated with a processing device may be shared by other processing devices.

[0089] The term "memory" as used herein is intended to include memory associated with a processor or CPU, such as, for example, RAM, ROM, fixed memory devices (e.g., hard drives), removable memory devices (e.g., diskettes), flash memory, etc. Such memory may be considered a computer-readable recording medium.

[0090] Furthermore, as used herein, the term "input / output device" or "I / O device" is intended to include, for example, one or more input devices (e.g., a keyboard, mouse, scanner, etc.) for inputting data into a processing unit and / or one or more output devices (e.g., speakers, displays, printers, etc.) for presenting results associated with a processing unit.

[0091] The foregoing should be understood in all respects as illustrative and exemplary, and not restrictive, and the scope of the invention disclosed herein should be determined not from the detailed description, but from the claims which are to be interpreted in accordance with the broadest possible interpretation permitted by the Patent Law. It should be understood that the embodiments shown and described herein are merely illustrative of the principles of the invention, and that various modifications may be made by those skilled in the art without departing from the scope and spirit of the invention. Various other feature combinations may be implemented by those skilled in the art without departing from the scope and spirit of the invention. Although aspects of the invention have been described above with the fine detail and particularity required by the Patent Law, the scope of the claims which are sought to be protected by Letters Patent are set forth in the appended claims.

Claims

1. A method for evaluating the feasibility of a WiFi Fine Tune Measurement (FTM) based ranging protocol for enterprise-grade location location with heterogeneity among operational parameters, comprising: Enabling distance measurements to multiple mobile devices operated by multiple users in an enterprise environment at multiple access points; tracking a mobile device of the plurality of mobile devices as it moves through the enterprise environment using an on-board sensor; measuring a plurality of distances to at least three of the plurality of access points by the mobile device at various points along a route; predicting a ranging offset of the mobile device by combining the plurality of distances with information related to the route, and the mobile device self-calibrating all of its ranging offsets on demand; The method comprising:

2. The method of claim 1 , wherein the mobile device performs FTM ranging at multiple time intervals to measure the multiple distances.

3. The method of claim 1 , wherein the ranging offset is calculated using a linear solver.

4. α i Let be the angular displacement of the mobile device, and d i Let (xc, yc) be the distance traveled by the mobile device on the path during a number of time intervals, (xc, yc) be the initial position of the mobile device, and R P is one of the distances and δ is the distance measurement offset, [0010] 4. The method of claim 3, wherein the new location on the path is arrived at based on a solution of:

5. The method of claim 1 , wherein the ranging offset is calculated using a second order solver.

6. α i Let be the angular displacement of the mobile device, and d i Let (x be the distance traveled by the mobile device on the path during a number of time intervals), AP , y AP ) is the initial position of the access point, and R P is one of the distances and δ is the distance measurement offset, [0025] The method of claim 5 , further comprising arriving at a new position on the path based on a solution of

7. (x AP , y AP ) is the initial position of the access point, and R 1 is the distance when the user of the mobile device was at position (0,0), the ranging offset is [0030] The method of claim 6, wherein the formula is given by:

8. 1. A non-transitory computer readable recording medium comprising a computer readable program for evaluating the feasibility of a WiFi Fine Tune Measurement (FTM) based ranging protocol for enterprise grade location determination with heterogeneity among operational parameters, the non-transitory computer readable recording medium comprising: When the computer-readable program is executed on a computer, the computer Enabling distance measurements to multiple mobile devices operated by multiple users in an enterprise environment at multiple access points; tracking a mobile device of the plurality of mobile devices as it moves through the enterprise environment using an on-board sensor; measuring a plurality of distances to at least three of the plurality of access points by the mobile device at various points along a route; predicting a ranging offset of the mobile device by combining the plurality of distances with information related to the route, and the mobile device self-calibrating all of its ranging offsets on demand; A non-transitory computer-readable recording medium for causing a computer to execute the method.

9. The non-transitory computer-readable medium of claim 8 , wherein the mobile device performs FTM ranging at multiple time intervals to measure the multiple distances.

10. The non-transitory computer-readable medium of claim 8 , wherein the ranging offset is calculated using a linear solver.

11. α i Let be the angular displacement of the mobile device, and d i Let (xc, yc) be the distance traveled by the mobile device on the path during a number of time intervals, (xc, yc) be the initial position of the mobile device, and R P is one of the distances and δ is the distance measurement offset, [0045] 11. The non-transitory computer readable medium of claim 10, wherein the path is arrived at at a new position based on a solution of:

12. The non-transitory computer-readable medium of claim 8 , wherein the ranging offset is calculated using a second order solver.

13. α i Let be the angular displacement of the mobile device, and d i Let (x be the distance traveled by the mobile device on the path during a number of time intervals), AP , y AP ) is the initial position of the access point, and R P is one of the distances and δ is the distance measurement offset, [0050] 13. The non-transitory computer readable medium of claim 12, wherein the path is reached at a new location based on a solution of:

14. (x AP , y AP ) is the initial position of the access point, and R 1 is the distance when the user of the mobile device was at position (0,0), the ranging offset is [006] 14. The non-transitory computer readable storage medium of claim 13, wherein:

15. A system for evaluating the feasibility of a WiFi Fine Tune Measurement (FTM) based ranging protocol for enterprise-grade location location with heterogeneity among operational parameters, comprising: A system includes a plurality of access points for measuring distances to a plurality of mobile devices operated by a plurality of users in an enterprise environment, tracking a mobile device of the plurality of mobile devices as it moves through the enterprise environment using an on-board sensor; at various points along a route, the mobile device measures a plurality of distances to at least three of the plurality of access points; combining the plurality of distances with information related to the route to predict a ranging offset for the mobile device; A system in which the mobile device self-calibrates all of its ranging offsets on demand.

16. The system of claim 15 , wherein the mobile device performs FTM ranging at multiple time intervals to measure the multiple distances.

17. The system of claim 15 , wherein the ranging offset is calculated using a linear solver.

18. α i Let be the angular displacement of the mobile device, and d i Let (xc, yc) be the distance traveled by the mobile device on the path during a number of time intervals, (xc, yc) be the initial position of the mobile device, and R P is one of the distances and δ is the distance measurement offset, [0070] 20. The system of claim 17, wherein the new location on the path is arrived at based on a solution of:

19. The system of claim 15 , wherein the ranging offset is calculated using a second order solver.

20. α i Let be the angular displacement of the mobile device, and d i Let (x be the distance traveled by the mobile device on the path during a number of time intervals), AP , y AP ) is the initial position of the access point, and R P is one of the distances and δ is the distance measurement offset, [0080] 20. The system of claim 19, wherein the new location on the path is arrived at based on a solution of:

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