Method and system for mobile device localization region in trusted-GPS region

A method and system for localizing a mobile device having a processor and a memory. The method comprises, using the processor, localizing the mobile device during navigation of a sequence of positions along an indoor area based on a data fusion of fingerprint data, detecting, using the processor, a boundary of a trusted-global positioning system (trusted-GPS) positioning region within the indoor area, and upon navigating to the boundary, localizing the mobile device based on GPS position data acquired at the memory of the mobile device.

TECHNICAL FIELD

The disclosure herein relates to the field of mobile device navigation and positioning.

BACKGROUND

Users of mobile devices are increasingly using and depending upon indoor positioning and navigation applications and features. Seamless, accurate and dependable indoor positioning can be difficult to achieve using satellite-based navigation systems when the latter becomes unavailable or sporadically available, such as within enclosed or partly enclosed urban infrastructure and buildings, including hospitals, shopping malls, airports, universities and industrial warehouses. To address this problem, indoor navigation solutions increasingly rely on sensors including accelerometers, gyroscopes, and magnetometers which may be commonly included in mobile phones and other mobile devices. Wireless communication signal data, ambient barometric data, mobile device inertial data and magnetic field data may be measured applied in localizing a mobile device along a route traversed within indoor infrastructure.

DETAILED DESCRIPTION

Embodiments herein recognize that mobile devices used for indoor navigation must perform with a degree of responsiveness that meets or exceeds user expectations. Among other technical effects and advantages, embodiments herein provide solutions which are directed to using indoor navigation solutions in a manner that enhances responsiveness and timeliness while preserving mobile device resources-usage, such as processor and memory resources, in a most economical manner to preserve longevity of mobile device power in a charged state. Embodiments herein also recognize that, among the various data inputs to user indoor navigation such as wireless signal data, inertial data, magnetic data, barometric and other data, wireless signal data processing consumes predominantly more than the other data input processing, typically by a significant margin. Also, depending on the quality and performance of the particular wireless infrastructure deployed in a building or indoor facility, a lower quality infrastructure may lead to delays in wireless signal processing at the mobile device, and thus delays in localizing the mobile device, to the dissatisfaction of a mobile device user. The latter adverse effect may become even more pronounced in situations where the mobile device is an older type, having a slower processor and a lower amount of random access memory relative to newer mobile devices.

Provided is a method of localizing a mobile device having a processor and a memory. The method comprises, using the processor, localizing the mobile device during navigation of a sequence of positions along an indoor area based on a data fusion of fingerprint data, detecting, using the processor, a boundary of a trusted-global positioning system (trusted-GPS) positioning region within the indoor area, and upon navigating to the boundary, localizing the mobile device based on GPS position data acquired at the memory of the mobile device.

Also provided is a mobile device comprising a processor and a memory storing a set of instructions. The instructions are executable in the processor to localize the mobile device during navigation of a sequence of positions along an indoor area based on a data fusion of fingerprint data, detect a boundary of a trusted-global positioning system (trusted-GPS) positioning region within the indoor area, and upon navigating to the boundary, localize the mobile device based on GPS position data acquired at the memory of the mobile device.

While GPS and cellular signals are typically inaccurate or unreliable indoors, often times there may be pockets where it can be more trustworthy, for example, near a skylight or near large windows. The disclosure herein proposes a system and method for automatically identifying these trustworthy areas via crowdsourced mobile device user data. The disclosure herein provide for knowledge of these areas to be applied for indoor positioning purposes or for license to other parties as regions in which GPS-based geofences can be viable, such as for commercial promotions purposes.

The disclosure herein provide for discovery of favorable areas where GPS positioning be relied upon as a light, cost-effective alternative to data fusion techniques for indoor positioning purposes, such as where full indoor or infrastructure coverage using wireless signals and magnetic field measurements might not be available or reliable, and also to establish a light-weight cost-effective geofence.

Such defined or designated GPS regions can be used as geofences for commercial promotions purposes, since identification as a GPS-trusted region where the geofence is accurate and can be confidently determined via GPS and cellular data alone, without the need of any additional data fusion processing applied for indoor navigation or positioning. Yet further, the GPS-trusted region positioning data may be applied with a commensurably higher weighting when localizing, in conjunction with some subset of data fusion based on fingerprint input parameters, within the GPS-trusted region geofence, at least to minimize mobile device processor and memory resources usage, with consequent mobile device responsiveness as experience by the carrying user within the indoor area.

The term fingerprint, variously referred to herein as fingerprint data, in one embodiment constitutes time-stamped, time-correlated, individual measurements of any combination of received wireless communication signal strength information, magnetic field information (strength, direction) or barometric pressure information at known, fixed locations within an area, including an indoor area. In other words, a fingerprint includes a correlation of sensor and signal information (including, but not necessarily limited to wireless signal strength, magnetic or barometric information inertial sensor information) at a given instance in time, at a unique position along a sequence of positions that constitute a navigation path traversed by the mobile device. Additionally, given that sampling times and sampling rates applied to particular device sensors may be different, the signal and sensor information as measured may be time-averaged across particular periods of time, with the time-averaged value being used to represent the signal information at any given instance of time within that particular period of time in which the signal information is time-averaged.

In a crowd sourcing-based approach, users may be provided an indoor positioning mobile device application and may be encouraged to walk around the area of interest, such as an indoor shopping mall. At various known, fixed locations within the area, events, also referred to herein as occurrence events, may be triggered and logged. Based on the logged data, an offline estimation of the user trajectory may be determined, and corresponding to known fixed locations, fingerprint measurements may be correlated with respective indoor locations along a trajectory, or trajectory segments, along which a user's mobile device traverses while within the area. As more trajectories from numerous other users are accumulated and logged, the averaging of user results may be used to accomplish a fingerprint mapping of the area or region.

In particular, the crowd sourcing based embodiments described here advantageously avoid the need for tedious and expensive specially-purposed, dedicated mapping of trusted-GPS regions of an indoor area, and result in a more accurate map representation of a targeted environment. Users incentives, for example, may be offered, to encourage random mobile device users to participate using their mobile device indoor navigation application.

Some embodiments described herein can generally require the use of computing devices, including processor and memory resources. For example, one or more embodiments described herein may be implemented, in whole or in part, on computing devices such as servers, desktop computers, mobile devices including cellular or smartphones, laptop computers, wearable devices, and tablet devices. Memory, processing, and network resources may all be used in connection with the establishment, use, or performance of any embodiment described herein, including with the performance of any method or with the implementation of any system.

System Description

FIG. 1illustrates, in an example embodiment, a system for generating and deploying a trusted-GPS map feature of an indoor area. Server101, includes trusted-GPS logic module105, and is communicatively connected via communication network104to a plurality of computing and communication mobile devices102a-n, also referred to herein as mobile device(s)102a-n. Mobile devices102a-ninclude navigation logic module106, which in one embodiment, may be included in an indoor positioning, or indoor navigation, software application downloaded and installed at individual ones of mobile devices102a-n.

FIG. 2illustrates an example architecture of a computing and communication mobile device102, representative of a plurality of mobile devices102a-nfor acquisition of fingerprint data in conjunction with GPS data for particular positions or locations within the indoor area. As used herein, the term mobile device102refers to any singular mobile device among mobile devices102a-n. In one embodiment, mobile device102may correspond to, for example, a cellular communication device (e.g., smartphone, tablet, etc.) that is capable of telephony, messaging, and/or data computing services. In variations, mobile device102can correspond to, for example, a tablet or a wearable computing device. Mobile device102may include processor201, memory202, display screen203, input mechanisms204such as a keyboard or software-implemented touchscreen input functionality, barcode, QR code or other symbol- or code-scanner input functionality. Mobile device102may include global positioning system (GPS) module207, with the GPS and cellular data acquired capable of providing particular locations of mobile device102. Mobile device102may include sensor functionality by way of sensor devices205. Sensor devices205may include any of inertial sensors (accelerometer, gyroscope), magnetometer or other magnetic field sensing functionality, and barometric or other environmental pressure sensing functionality. Mobile device102may also include capability for detecting and communicatively accessing wireless communication signals, including but not limited to any of Bluetooth, Wi-Fi, RFID, and GPS signals. Mobile device102further includes the capability for detecting and measuring a received signal strength of the wireless communication signals. In particular, mobile device102may include location determination capability such as by way of GPS module205, and communication interface206for communicatively coupling to communication network104, such as by sending and receiving cellular data over data channels and voice channels.

Navigation logic module106includes instructions stored in memory202of mobile device102. In embodiments, navigation logic module106may be included in a mobile device navigation application program stored in memory202of mobile device102for acquiring fingerprint data within an area by any of plurality of mobile devices102a-n. The area may be an indoor area within a shopping mall, an airport, a warehouse, a university, or any at least partially enclosed building. Acquisition of the fingerprint data may be automatically triggered at respective ones of mobile devices102a-nupon an event occurrence. The event occurrence may consist of a user of mobile device102redeeming a promotion coupon at a merchant within a shopping mall, scanning a barcode, using an RFID tag, or upon the mobile device102becoming present at specific predetermined locations within the area. The occurrence event may be also based on detecting a proximity beacon wireless signal, in some examples. Acquisition of the fingerprint data by a user's mobile device102may thus be automatically triggered upon the event occurrence at any one of a predetermined set of fixed positions within the area.

In this manner, a user of mobile device102may, in effect, passively assist in creating a trusted-GPS region by acquiring fingerprint data, then allowing uploading or other transfer of the acquired fingerprint data to server101for further processing. The fingerprint data may be acquired at least in part using sensor devices205of the mobile devices, including but not limited to an accelerometer, a gyroscope, a magnetometer, a barometer, and a wireless signal strength sensor. The fingerprint data may include any one of orientation data, a magnetic field data including strength and direction, received wireless signal strength data, barometric pressure data, and also GPS location data at a position within the area for respective mobile devices.

As the fingerprint data acquired at mobile device102is time-stamped and the data collection via navigation logic module106operates in a distributed manner, the fingerprint data may be cached on the local memory202and subsequently batch transferred as a compressed data file for post-processing at server101, in some embodiments. Navigation logic module106, in effect, operates to pre-process fingerprint data and extract key features which can assist in the mobile device102trajectory reconstruction during the post processing step at server101. The pre-processing step at navigation logic module106may include counting the number of steps taken by a user of mobile device102, estimating the step length of each step, estimating the heading direction for each step, as well as, recording the time-averaged and time-stamped magnetic field information and wireless radio signals, and monitoring for, and logging, occurrence of any triggered event/tag-based data that enables the trajectory of mobile device102to be best matched a physical map of the area that includes known fixed objects at unique locations.

FIG. 3illustrates an example architecture of a server computing device for generating and deploying a trusted-GPS map feature. Server101, in an embodiment architecture, may be implemented on one or more server devices, and includes processor301, memory302which may include a read-only memory (ROM) as well as a random access memory (RAM) or other dynamic storage device, display device303, input mechanisms304and communication interface305for communicative coupling to communication network104. Processor301is configured with software and/or other logic (such as from trusted-GPS logic module105) to perform one or more processes, steps and other functions described with implementations, such as described byFIGS. 1 through 4herein. Processor301may process information and instructions stored in memory302, such as provided by a random access memory (RAM) or other dynamic storage device, for storing information and instructions which are executable by processor301. Memory302also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor301. Memory302may also include the ROM or other static storage device for storing static information and instructions for processor301; a storage device, such as a magnetic disk or optical disk, may be provided for storing information and instructions. Communication interface305enables server101to communicate with one or more communication networks104(e.g., cellular network) through use of the network link (wireless or wired). Using the network link, server101can communicate with computing devices102a-n.

Trusted-GPS logic module105of server101may include instructions stored in RAM of memory302, and includes fingerprint data acquisition module305, correlation module306, and map deployment module307.

Processor301uses executable instructions stored in fingerprint data acquisition module305to acquire fingerprint data within the area by a plurality of mobile devices102a-n. The area may be an indoor area within a shopping mall, an airport, a warehouse, a university, or any at least partially enclosed building. In embodiments, the fingerprint data, as acquired from mobile devices102a-n, further includes respective time-stamps, whereby the orientation, the magnetic field strength and direction, the received wireless signal strength, the barometric pressure, and the position data can be time-correlated for any given position along a trajectory or trajectory segment of the mobile devices, in accordance with the respective time-stamps. Additionally, when the sampling times and sampling rates applied to particular ones of device sensors205are different, the signal and sensor information as measured may be time-averaged across particular periods of time, with the time-averaged value being used to represent the signal information at any given instance of time within that particular period of time in which the signal information is time-averaged.

Processor301uses executable instructions stored in correlation module306to generate a distribution of positioning data points of the indoor area for which a correlation between the at least a first set of fingerprint data and the at least a first set of GPS position data for respective ones of the sequence of positions exceeds a threshold correlation value. The term “position” as used herein refers to a coordinate location, and may be expressed in local or global (X, Y) coordinate terms. In some embodiments, the coordinates may further include a Z coordinate representing a height, for example associated with a given floor within a multi-floor building, and thus expressed in (X, Y, Z) coordinate terms. Further processing, via the instructions constituting correlation module306executable in processor301, may apply for a second set of fingerprint data and the calibrated data points to generate an updated distribution of calibrated data points.

Processor301uses executable instructions stored in map deployment module307when the distribution exceeds at least one of a predetermined and a dynamically updated threshold density of positioning data points, deploying the distribution as the trusted-GPS positioning map of the indoor area. A density determination algorithm may be applied, in one embodiment, to establish the predetermined threshold density based on validating the distribution of data points as sufficient for deployment, representing the trusted-GPS positioning map of the area. In another embodiment, the threshold density for deployment may be dynamically determined, and dynamically updated, based on updating at least one of the density of calibration data points and the consistency amongst the calibration data points relative to a neighboring area contiguous with the target area. Dynamically updating the threshold density in the latter manner allows the system to automatically detect and correct potential calibration inconsistencies prior to deploying the calibrated positioning map of the area. The density determination algorithm may be employed in conjunction with an artificial neural network to validate when a sufficient number of fingerprint and GPS positioning data points have been collected and accumulated for specific areas or regions within the areas. In particular, this process can also assist in identifying pedestrian traffic patterns and traffic densities for particular areas and times within the area or the shopping mall, as well as to provide the capability to assess whether or not a sufficient amount of data has been collected to complete the trusted-GPS region map deployment process. In the present embodiment, the one-time artificial neural network processing initializes the fingerprint data. Moreover, based on the artificial neural network processing, a dynamic incremental fingerprint updating scheme may be employed to dynamically maintain up-to-date fingerprint calibration data sets.

Methodology

FIG. 4illustrates, in an example embodiment, a method of operation400in deploying a trusted-GPS map feature. In describing examples ofFIG. 4, reference is made to the examples ofFIGS. 1-3for purposes of illustrating suitable components or elements for performing a step or sub-step being described.

Examples of method steps described herein are related to the use of server101for implementing the techniques described herein. According to one embodiment, the techniques are performed by trusted-GPS logic module105of server101in response to the processor301executing one or more sequences of software logic instructions that constitute trusted-GPS logic module105. In embodiments, trusted-GPS logic module105may include the one or more sequences of instructions within sub-modules including fingerprint data acquisition module305, correlation module306and map deployment module307. Such instructions may be read into memory302from machine-readable medium, such as memory storage devices. Execution of the sequences of instructions contained in fingerprint data acquisition module305, correlation module306and map deployment module307of trusted-GPS logic module105in memory302causes processor301to perform the process steps described herein. In alternative implementations, at least some hard-wired circuitry may be used in place of, or in combination with, the software logic instructions to implement examples described herein. Thus, the examples described herein are not limited to any particular combination of hardware circuitry and software instructions.

At step410, receiving, at memory302of server computing device101, at least a first set of fingerprint data and at least a first set of GPS position data for a sequence of positions traversed within an indoor area by at least a first mobile device101a-n. The area may be an indoor area within a shopping mall, an airport, a warehouse, a university, or any at least partially enclosed building. Acquisition of the first and second sets of fingerprint data may be automatically triggered at respective mobile devices upon an event occurrence. The event occurrence may consist of a user redeeming a coupon at a merchant within a shopping mall, scanning a barcode, using an RFID tag, or may be based on accessibility of a proximity beacon wireless signal, in some examples. Acquisition of the fingerprint data by a user's mobile device may thus be automatically triggered upon the event occurrence at any one of a predetermined set of fixed positions within the area. In this manner, a user of mobile device102may, in effect, passively assist in the positioning data points calibration process by acquiring fingerprint data, then allowing uploading or other transfer of the acquired fingerprint data to server101for further processing. The fingerprint data may be acquired using sensor devices205of the mobile devices, including but not limited to an accelerometer, a gyroscope, a magnetometer, a barometer, and a wireless signal strength sensor, in conjunction with GPS positioning data using GPS module107. The fingerprint data may include any one of an orientation, a magnetic field strength and direction, a received wireless signal strength, a barometric pressure, and an optical line of sight data at a position within the area for respective mobile devices.

In embodiments, the fingerprint data, as acquired from the mobile devices, further includes respective time-stamps, whereby the orientation and other inertial sensor data, the magnetic field strength and direction, the received wireless signal strength, the barometric pressure, and the position data can be time-correlated in accordance with the time-stamps with respect to any given position along a sequence of positions describing a trajectory or trajectory segment of the mobile devices101a-n. Additionally, given that sampling times and sampling rates applied to particular ones of device sensors205may be different, the signal and sensor information as measured may be time-averaged across particular periods of time, with the time-averaged value being used to represent the signal information at any given instance of time within that particular period of time in which the signal information is time-averaged.

At step420, generating, using the processor, a distribution of positioning data points of the indoor area for which a correlation between the at least a first set of fingerprint data and the at least a first set of GPS position data for respective ones of the sequence of positions exceeds a threshold correlation value. The terms position or positioning as used herein refers to a coordinate location and may be expressed in local or global (X, Y, Z) coordinate terms.

At step430, using the executable instructions of map deployment module305, when the distribution exceeds at least one of a predetermined and a dynamically updated threshold density of positioning data points for deploying the distribution as the trusted-GPS positioning map of the indoor area. In one embodiment, the threshold density for deployment may be dynamically determined, and dynamically updated. Dynamically updating the threshold density allows the system to automatically detect and correct potential calibration inconsistencies prior to deploying the trusted-GPS map of the area. A density determination algorithm may be applied, in one embodiment, to establish the predetermined threshold density based on validating the distribution of data points as sufficient for deployment.

In some embodiments, the indoor area may include one of a shopping mall, an airport, a warehouse, a campus building and an at least partially enclosed building.

The acquisition of the first and second sets of fingerprint data may be automatically triggered at the at least one mobile device upon an event occurrence, the event occurrence comprising at least one of redeeming a coupon, scanning a barcode, and using an RFID tag.

The fingerprint data may include at least two of wireless signal data, inertial data, magnetic data, barometric data and optical data that are time-stamped and time-correlated for respective positions in the sequence of positions.

The threshold correlation value may be a predetermined value, in one embodiment, a threshold correlation of at least 85% between respective coordinate positions as determined via the GPS module of the mobile device and the mobile device localization based on the data fusion.

A density determination algorithm may be applied to establish a predetermined threshold density as sufficient for deploying as the trusted-GPS positioning map of the indoor area.

The threshold density of positioning data points may be dynamically updated in conjunction with an artificial neural network to validate when a sufficient number of positioning data points have been accumulated for at least a portion of the indoor area.

In one embodiment, an outer boundary described by the distribution of positioning data points may be defined as a trusted-GPS boundary. The outer boundary may circumscribe an enclosed region within the indoor area, in another variation. In yet another embodiment, the enclosed region may be designated as a trusted-GPS region.

FIG. 5illustrates, in an example embodiment, a method500of utilizing a trusted-GPS region for localizing mobile device102. In describing examples ofFIG. 5, reference is made to the examples ofFIGS. 1-4for purposes of illustrating suitable components or elements for performing a step or sub-step being described.

Examples of method steps described herein are related to localization of mobile device102using the techniques described herein. According to one embodiment, the techniques are performed by navigation logic module106of mobile device102in response to the processor201executing one or more sequences of software logic instructions that constitute navigation logic module106. In embodiments, navigation logic module106may be incorporated into an indoor navigation application downloaded into memory202of mobile device102, and executable in processor201of mobile device102.

At step510, using the processor201, localizing the mobile device102during navigation of a sequence of positions along an indoor area based on a data fusion of fingerprint data.

At step520, detecting, using the processor201, a boundary of a trusted-global positioning system (trusted-GPS) positioning region within the indoor area.

At step530, upon navigating to the boundary, localizing the mobile device102based on GPS position data acquired at the memory of the mobile device.

In one aspect, the data fusion is based on fingerprint data that includes at least two of wireless signal data, inertial data, magnetic data, barometric data and optical data, the fingerprint data being time-stamped and time-correlated for respective positions in the sequence of positions.

In embodiments, the fingerprint data is acquired using a set of sensors of the mobile device, the sensors including at least one of an accelerometer, a gyroscope, a magnetometer, a barometer, and a wireless signal strength sensor.

The fingerprint data may include at least one of an orientation, a magnetic field strength and direction, a received wireless signal strength, and a barometric pressure. The GPS position data may be acquired using a GPS module of the mobile device.

In an embodiment, upon navigating to the boundary, the data fusion input parameters may be modified to exclude at least one of wireless signal data, magnetic data, and inertial data, or at least to exclude the processing thereof. The modifying may, alternately or additionally, be based on switching off at least one sensor of the mobile device. The modifying may further be based on terminating processing of fingerprint databased on at least one wireless signal sensor of the mobile device, in one aspect.

The boundary may encompass the trusted-GPS positioning region, and the method may further comprise maintaining the modified state while navigating within the trusted-GPS positioning region of the indoor area.

It is contemplated for embodiments described herein to extend to individual elements and concepts described herein, independently of other concepts, ideas or system, as well as for embodiments to include combinations of elements recited anywhere in this application. Although embodiments are described in detail herein with reference to the accompanying drawings, it is to be understood that the invention is not limited to those precise embodiments. As such, many modifications and variations will be apparent to practitioners skilled in this art. Accordingly, it is intended that the scope of the invention be defined by the following claims and their equivalents. Furthermore, it is contemplated that a particular feature described either individually or as part of an embodiment can be combined with other individually described features, or parts of other embodiments, even if the other features and embodiments make no mention of the particular feature. Thus, the absence of describing combinations should not preclude the inventor from claiming rights to such combinations.