阅读说明：本技术 用于定位建筑物出入口的系统和方法 (System and method for locating building doorway ) 是由 徐海良 束纬寰 于 2017-06-20 设计创作，主要内容包括：提供了一种用于定位建筑物出入口的系统和方法。该方法可以包括：基于移动设备获取的运动数据和运动模型,确定移动设备的轨迹(S602),获取移动设备的轨迹的第一终端位置(S604),检测移动设备进入或离开建筑物(S606),并且当检测到所述移动设备进入或离开建筑物时,确定所述轨迹的第二终端位置,将所述第二终端位置作为出入口的位置(S608)。(A system and method for locating a doorway of a building are provided.A method may include determining a trajectory of a mobile device based on motion data and a motion model acquired by the mobile device (S602), acquiring a th terminal position of the trajectory of the mobile device (S604), detecting entrance or exit of the mobile device into or out of the building (S606), and determining a second terminal position of the trajectory when the entrance or exit of the mobile device into or out of the building is detected, the second terminal position being a position of the doorway (S608).)

1, a computer-implemented method for locating a building doorway, comprising:

determining a track of a mobile device based on motion data and a motion model obtained by the mobile device;

acquiring th terminal position of the track of the mobile device through a positioning device;

detecting entry or exit of the mobile device into a building; and

and when the mobile equipment is detected to enter or leave the building, determining a second terminal position of the track, and taking the second terminal position as the position of the entrance.

2. The method of claim 1, wherein the terminal location is a destination of a trip taken by a user carrying the mobile device, and wherein the mobile device subsequently enters the building.

3. The method of claim 1, wherein the mobile device leaves the building and the terminal location is a starting point of a trip taken by a user carrying the mobile device.

4. The method of claim 1, further comprising identifying the building based on the terminal location.

5. The method of claim 1, wherein detecting the mobile device entering or leaving the building further comprises:

determining a reference value based on the sensing signal acquired by the mobile equipment;

comparing the reference value to a threshold value; and

determining that the mobile device enters or leaves the building when the reference value is greater than the threshold value.

6. The method of claim 5, wherein the sensor signal comprises at least of air pressure, temperature, light intensity, wireless fidelity signal, or base station signal.

7. The method of claim 1, wherein determining the trajectory of the mobile device further comprises:

receiving the motion data acquired by the mobile device;

generating at least two motion vectors using a motion model based on the motion data; and

determining the trajectory of the mobile device by synthesizing the motion vectors.

8. The method of claim 7, wherein the step of includes training the motion model using at least sets of training motion data and corresponding trajectories.

9. The method of claim 7, wherein the motion model further comprises at least two sub-models, each sub-model corresponding to a location of a user carrying the mobile device.

10. The method of claim 7, further comprising:

selecting a sub-model based on the motion data; and

determining the motion vector using the sub-model based on the motion data.

a system for locating a doorway of a building comprising:

a memory configured to store a motion model; and

a processor configured to:

determining a track of a mobile device based on motion data and a motion model obtained by the mobile device;

acquiring th terminal position of the track of the mobile device;

detecting the mobile device entering or leaving the building; and

and when the mobile equipment is detected to enter or leave the building, determining a second terminal position of the track, and taking the second terminal position as the position of the entrance.

12. The system of claim 11, wherein said terminal location is a destination of a trip by a user carrying said mobile device, and wherein said mobile device subsequently enters said building.

13. The system of claim 11, wherein the mobile device leaves the building and the terminal location is a starting point of a trip taken by a user carrying the mobile device.

14. The system of claim 11 wherein the processor is further configured to identify the building based on the terminal location.

15. The system of claim 11, wherein the processor is further configured to:

determining a reference value based on the sensing signal acquired by the mobile equipment;

comparing the reference value to a threshold value; and

determining that the mobile device enters or leaves the building when the reference value is greater than the threshold value.

16. The system of claim 15, wherein the sensor signal comprises at least of air pressure, temperature, light intensity, wireless fidelity signal, or base station signal.

17. The system of claim 11, wherein the processor is further configured to:

receiving the motion data acquired by the mobile device;

generating at least two motion vectors using a motion model based on the motion data; and

determining the trajectory of the mobile device by synthesizing the motion vectors.

18. The system of claim 17, wherein the processor is further configured to train the motion model using at least sets of training motion data and corresponding trajectories.

19. The system of claim 17, wherein the motion model further comprises at least two sub-models, each sub-model corresponding to a location of a user carrying the mobile device.

20, non-transitory computer-readable storage media comprising instructions which, when executed by at least processors of a positioning system, cause the positioning system to perform a method for positioning a building doorway, the method comprising:

determining a track of a mobile device based on motion data and a motion model obtained by the mobile device;

acquiring th terminal position of the track of the mobile device;

detecting the mobile device entering or leaving the building; and

and when the mobile equipment is detected to enter or leave the building, determining a second terminal position of the track, and taking the second terminal position as the position of the entrance.

Technical Field

The present application relates to location technology, and more particularly, to a system and method for locating building doorways.

Background

It is important to accurately locate the doorway of a building, such as a house, building, parking lot, or any other structure. For example, to create a layout of a building, the doorway is the starting point of all the routes within the building. Further, if the destination is a large building located at an intersection, the entrance and exit position should be used as an end point of the navigation route.

Traditionally, building doorways have been manually positioned. For example, the location of the locating device may be determined and manually calibrated to designate the location of the building doorway. Locating a large number of building entrances and exits is not only time consuming, but also expensive to employ technicians to perform the location manually.

When a person carrying a mobile device (e.g., a smartphone, tablet, smart watch, etc.) walks into or out of a building, building doorways may be located by collecting location data and motion data of the mobile device. However, the location data of the smart device relies heavily on Global Positioning System (GPS) signals, which can only be received by the mobile device when the GPS positioning module of the mobile device is active.

Embodiments of the present application provide improved systems and methods for locating building doorways even when the GPS location module is off or the GPS signal is too weak to locate.

Disclosure of Invention

aspects of the present application provide methods for locating a doorway of a building, which may include determining a trajectory of a mobile device outside a building based on motion data and a motion model acquired by the mobile device, acquiring a th terminal position of the trajectory of the mobile device through a locating device, detecting entry or exit of the mobile device into or out of the building, and determining a second terminal position of the trajectory when entry or exit of the mobile device into or out of the building is detected, the second terminal position being a position of the doorway.

Another aspect of the application provides a system for locating a doorway of a building, the system may include a storage medium to store a motion model, a processor configured to determine a trajectory of a mobile device outside the building based on motion data and the motion model acquired by the mobile device, acquire a th terminal position of the trajectory of the mobile device, detect entry or exit of the mobile device into the building, and determine a second terminal position of the trajectory when the entry or exit of the mobile device into the building is detected, the second terminal position being a position of the doorway.

A further aspect of the application provides non-transitory computer-readable storage media storing a set of instructions, including when executed by at least processors of a positioning system, cause the positioning system to perform a method for locating a doorway of a building, the method including determining a trajectory of a mobile device based on motion data and a motion model acquired by the mobile device, acquiring a th terminal position of the trajectory of the mobile device, detecting entry or exit of the mobile device into or out of the building, and when the entry or exit of the mobile device into or out of the building is detected, determining a second terminal position of the trajectory, the second terminal position being a position of the doorway.

It is to be understood that both the foregoing -generic description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

Drawings

FIG. 1 is a schematic diagram illustrating exemplary relationships between terminal devices, gates, and vehicles, according to embodiments of the present application;

FIG. 2 is an exemplary system for locating building doorways, shown in accordance with embodiments of the present application;

FIG. 3 is a block diagram of an exemplary system for locating building doorways according to embodiments of the present application;

FIG. 4 is a block diagram of an exemplary trajectory determination unit according to embodiments of the present application;

FIG. 5 is an exemplary coordinate system of a mobile device shown in accordance with embodiments of the present application;

FIG. 6 is a flow chart of an exemplary method for locating building doorways according to embodiments of the present application, and

FIG. 7 is a flow diagram illustrating an exemplary process for generating a trajectory for a mobile device according to embodiments of the present application.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.

Fig. 1 is a schematic diagram illustrating exemplary relationships between terminal devices, gates, and vehicles, according to embodiments of the present application.

, the driver's terminal device has a GPS location module turned on for GPS navigation, for example, when the driver installs an on-line vehicle-appointment platform application (e.g., a drip application) on his/her terminal device, the application defaults to turning on the GPS location module so that the location of the corresponding vehicle can be uploaded to the server of the on-line vehicle-appointment platform system.

Typically, a user may call a vehicle (e.g., a taxi or a private car) to start a trip using an online car appointment platform on a mobile device (e.g., a user device) while the user is still within a building (e.g., an office building, a mall, a restaurant, a residence or apartment building, etc.). A user carrying a mobile device may pass through an exit of a building to a ride pick-up location and wait for a vehicle. Similarly, after the trip is over, the user carrying the mobile device may exit the vehicle and enter the building through the entrance of the building. Thus, the user creates a trajectory (i.e., a trajectory of the mobile device) between the entrance/exit and the vehicle.

Fig. 1 shows a trajectory 110 of the mobile device 102 between the doorway 104 (entrance or exit) and the vehicle 106. When the trajectory 110 is a trajectory toward the building doorway 104 (i.e., entrance), the position of the vehicle 106 is a start position of the trajectory 110, and the position of the doorway 104 is an end position of the trajectory 110.

Similarly, when the trajectory 110 is a trajectory out of the building doorway 104 (i.e., an exit), the position of the vehicle 106 is a terminal position of the trajectory 110, and the position of the doorway 104 is a starting position of the trajectory 110. Thus, given the location of the vehicle 106 and the trajectory 110, the location of the doorway 104 may be determined based on whether the mobile device 102 is moving toward the doorway 104 or away from the doorway 104.

Fig. 2 is an exemplary system 200 for locating building doorways, shown in accordance with embodiments of the present application.

The system 200 may be a general purpose server or a proprietary device that communicates with the mobile device 102 and the vehicle 106.

As with embodiment of the present application, the mobile device 102 may include at least two sensors and/or signal receivers for acquiring sensor signals to determine a trajectory of the mobile device 102.

The sensors may include air pressure sensors, temperature sensors, light sensors, and the like. The sensors may also include motion sensors, such as magnetometers, gyroscopes, gravity sensors, acceleration sensors, and the like, and the motion sensors may be configured to receive motion data, such as magnetic field strength, orientation, gravity values, acceleration values, and the like of the mobile device 102.

The signal receiver may be configured to receive wireless fidelity (WiFi) signals, Bluetooth signals, base station signals, etc. the base station signals may also include global systems for mobile communications (GSM) signals, Code Division Multiple Access (CDMA) signals, Wideband Code Division Multiple Access (WCDMA) signals, Long Term Evolution (LTE) signals, etc. accordingly, the sensing signals may include at least of atmospheric pressure, temperature, light intensity, magnetic field values, orientation, gravity values, acceleration values, WiFi signals, base station signals, etc.

The vehicle 106 may upload the trip information to the system 200. For example, the trip information may include the identity of the user, the location of the vehicle 106, and the like. The identity of the user may include information of a user account that is logged on to the user's mobile device 102. The location of the vehicle 106 may include any location during the trip, such as a start point of the trip, a destination point of the trip, and so forth. The vehicle 106 may obtain the travel information via a driver terminal device (not shown) having an online appointment platform application (e.g., a drip application) or a built-in device (not shown). The driver terminal device or built-in device may include a GPS location module for determining the location of the vehicle 106. Thus, while the trip is still ongoing, the mobile device 102 may be located based on the location of the vehicle 106 even if the GPS location module of the mobile device 102 is not active or has a weak signal.

Consistent with embodiments of the present application, the system 200 may locate a building doorway based on motion data and sensing signals of the mobile device 102 and travel information of the vehicle 106 fig. 3 is a block diagram of an exemplary system for locating a building doorway according to embodiments of the present application.

As shown in FIG. 3, system 200 may include a processor 300, a communication interface 302, and a memory 312. processor 300 may also include a plurality of functional modules, such as a trajectory determination unit 304, a terminal location acquisition unit 306, a pass entrance detection unit 308, and an entrance location unit 310. these modules (and any corresponding sub-modules or sub-units) may be functional hardware units (e.g., portions of an integrated circuit) of processor 300 designed to perform or more functions with portions of other components or programs (stored on a computer-readable storage medium) executed by processor 300. although FIG. 3 shows all of units 304-310 within processors 300, it is contemplated that these units may be distributed among multiple processors that are located near or remote from each other. System 200 may be implemented on the cloud, terminal device 102, or on a separate computer/server.

The communication interface 302 may be in communication with the mobile device 102 and the vehicle 106, and is configured to receive motion data and sensing signals of the mobile device 102 and trip information of the vehicle 106.

The memory 312 may be implemented as any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic memory, flash memory, or a magnetic or optical disk.

As described above, to locate a building doorway, the trajectory 110 of the mobile device 102 and the location of the vehicle 106 may be obtained, and it may be determined whether the mobile device 102 enters or leaves the building.

At stage , also referred to as a "training phase," the trajectory determination unit 304 may generate and train a motion model using data associated with the mobile device 102, and at a second stage, also referred to as a "determination phase," the trajectory determination unit 304 may determine the trajectory 110 of the mobile device using the trained motion model, which may be the same mobile device 102 or another mobile device carried by the user.

In embodiments, the motion model may be generated during a training phase by a Pedestrian Dead Reckoning (PDR) method.

For example, when training begins, the user may be required to open the GPS location module of the mobile device 102 and carry the mobile device 102 with him via an application installed on the mobile device 102 (e.g., a drip application). for example, the mobile device 102 may be placed anywhere on the user, such as in his pocket, attached to his belt, or in another item carried by the user, such as a purse, arm band, etc.

It is contemplated that the GPS determined trajectory is merely an example of a determined trajectory, any method may be used to generate a determined trajectory so long as the trajectory is sufficiently accurate for training, at , indoor positioning methods may also generate a determined trajectory.

The application may also require that the user walk or jog segments of a predetermined distance so that training motion data may be collected by the motion sensor and a corresponding trajectory of the mobile device 102 may be determined by the GPS location module.

As shown in fig. 4, the trajectory determination unit 304 may include a motion data acquisition unit 402, a motion model training unit 404, a motion vector determination unit 404, and a trajectory synthesis unit 406.

In embodiments, mobile device 102 may collect training motion data and corresponding trajectories at a predetermined frequency (e.g., five times per second) during a training phase, and motion data acquisition unit 402 may receive the collected training motion data and determine trajectories in embodiments, a data collection window (e.g., three seconds) may be defined for collecting training motion data and trajectories.

The motion model training unit 404 may then train the motion model based on the received training motion data and the corresponding trajectory.

For example, the motion model training unit 404 may perform statistical analysis on each set of training motion data and corresponding trajectory to extract feature vectors and supervisory signals to train the motion model.

In embodiments, the feature vector may include statistics for each axis of the coordinate system of the mobile device 102 FIG. 5 is an exemplary coordinate system 500 for the mobile device 102 shown in accordance with embodiments of the present application.

As shown in fig. 5, the coordinate system 500 may be a cartesian coordinate system. The mobile device 102 may be designated as the origin of the coordinate system 500, the frontal direction of the mobile device 102 may be designated as the X-axis, the left direction may be designated as the Y-axis, and the orthogonal direction of the X-axis and the Y-axis may be designated as the Z-axis. It is contemplated that other coordinate systems may be employed on the mobile device 102.

Similarly to the motion data, the training motion data may also include at least a magnetic field strength, a direction, a gravity value, an acceleration value, etc. of the mobile device 102. by performing statistical analysis, the motion model training unit 404 may generate a mean, a variance, and a histogram of acceleration values in X, Y and each of the Z-axis.

Motion model training unit 404 also calculates the number of acceleration values in the X-axis that are greater than, equal to, and less than the acceleration values in the Y-axis, the number of acceleration values in the X-axis that are greater than, equal to, and less than the acceleration values in the Z-axis, and the number of acceleration values in the Y-axis that are greater than, equal to, and less than the acceleration values in the Z-axis, respectively. Thus, motion model training unit 404 generates three numbers for each axis based on the acceleration values and generates a total of nine components for the feature vector. Similarly, the motion model training unit 404 generates nine components based on the magnetic field values, respectively.

The motion model training unit 404 also generates mean, variance, and histogram (e.g., with 8 bins) of acceleration value differences between the X and Y axes, the X and Z axes, and the Y and Z axes, respectively. That is, the motion model training unit 404 may generate ten components for each pair of X and Y axes, X and Z axes, and Y and Z axes and a total of thirty components based on the acceleration value difference. Similarly, the motion model training unit 404 generates mean, variance, and histogram of magnetic field value differences between the X and Y axes, the X and Z axes, and the Y and Z axes. That is, the motion model training unit 404 may generate a total of thirty additional components based on the magnetic field strength difference. Therefore, the motion model training unit 404 generates sixty components for the feature vector based on the acceleration value difference and the magnetic field strength difference.

In summary, as described above, for each window, a feature vector having hundreds of thirty-eight components may be generated by motion model training unit 404.

It is contemplated that more or fewer components may be generated for each window by motion model training unit 404, depending on the algorithm used to process the training motion data. And more motion data may be used to generate the components of the feature vector in addition to the acceleration values and magnetic field strength.

For the same window above, the corresponding trajectory of the mobile device 102 may include the th position at the beginning of the window (x1, y1) and the second position at the end of the window (x2, y2), where x1 and x2 represent longitude values for the th and second positions and y1 and y2 represent latitude values for the th and second positions.

The motion model training unit 404 may generate at least two sets of feature vectors and supervisory signals based on the collected motion data and corresponding trajectories.

In embodiments, in the random forest algorithm, for example, each random forest may include 50 random trees, each random tree has a depth of 25, and the minimum number of samples on the leaves is 30.

The trained motion model may be stored in memory 312. It is contemplated that the mobile device 102 may be carried by the user in a variety of ways, such as holding the mobile device 102 in a hand, placing the mobile device 102 in a pocket, attaching the mobile device 102 to the user's arm, and the like. The trajectory determination unit 304 may generate a motion model comprising at least two sub-models, and each sub-model corresponds to a location of a user carrying the mobile device 102.

In embodiments of the present application, the motion data acquisition unit 402 of the trajectory determination unit 304 may also receive motion data acquired by the mobile device 102 in the determination phase the motion data acquisition unit 402 may send instructions to the mobile device 102 for collecting motion data at a predetermined event, for example, the motion data acquisition unit 402 may send instructions to the mobile device 102 for collecting motion data when the user calls a taxi using, for example, a dribble application.

It is contemplated that when the motion model includes at least two sub-models, the trajectory determination unit 304 may further select a sub-model based on the motion data and determine a motion vector using the sub-model based on the motion data.

Given at least two sets of feature vectors and supervisory signals computed from the motion data, at least two motion vectors may be generated, as described above, the mobile device 102 may collect training motion data and corresponding trajectories at a predetermined frequency, and thus the motion vector determination unit 404 may periodically generate motion vectors, for example, motion vectors may be generated every 200 milliseconds (ms) if data collection occurs five times per second.

The trajectory synthesis unit 406 of the trajectory determination unit 304 may then determine the trajectory of the mobile device 102 by synthesizing the motion vectors, hi embodiments, the motion vectors may be connected in sequence to generate the trajectory, hi another embodiment, the motion vectors may be assigned different weights₀As opposed to the previously generated motion vectors. Thus, different weights may be assigned to motion vectors based on when they are generated. For example, the currently generated motion vector V₀May be given a weight of "1", a motion vector V₁The motion vector generated earlier in time than the current one may be given a weight of "0.9", motion vector V₂Generated earlier in time than the motion vector V₁May be given a weight of "0.8", and the motion vector V_nGenerated earlier in time than the motion vector V_n-1May be given a weight of 1-0.1 × n in embodiments, n is 9, and may be generated by synthesizing based on the weights described aboveVector V of₀-V₉A weighted average vector is generated to generate the trajectory further .

It is contemplated that the above-generated trajectory is a composite vector representing motion from the th terminal position (i.e., the start position) to the second terminal position (i.e., the end position).

Referring back to FIG. 3, the terminal position acquisition unit 306 may acquire the th terminal position of the trajectory of the mobile device 102, as described above, the travel information of the vehicle 106 may include the identity of the user, which is associated with the user's mobile device 102, thus, the location of the mobile device 102 may be the same as the location of the vehicle 106 during the travel, in the example, when the user arrives at the destination point of the travel, the location of the vehicle 106 may be designated as the th terminal position, which is also the starting position of the trajectory of the mobile device 102. in the second example, after the user calls for vehicle service, when the vehicle 106 receives the user and starts the travel, the starting point of the travel may be designated as the th terminal position, that is, the th terminal position is the starting point of the travel made by the user carrying the mobile device 102.

Furthermore, since the start of the trip is usually close to the building, the building can be identified based on the th terminal location.

It is contemplated that the end position in the th example is a start position of the track and the th end position in the second example is an end position of the track, and thus, the th end position acquired by the end position acquisition unit 306 may be a start position or an end position of the track.

Given the location of the terminals on the trajectory, the doorway may be located by locating the doorway on the trajectory.

The location of the doorway may be determined accordingly when the mobile device 102 is detected to enter or leave the building, the air pressure, temperature, light intensity may change as the mobile device 102 enters or leaves the building, and the WiFi signals and base station signals received by the mobile device 102 may also change, for example, when the mobile device 102 enters the building, the mobile device 102 may be able to receive WiFi signals of WiFi access points disposed within the building.

, when the mobile device is detected to enter or leave the building, the entrance positioning unit 310 may further determine a second terminal position of the trajectory, and the second terminal position is used as the position of the entrance.

Thus, the system 200 can automatically locate a building doorway based on the trajectory of the user's mobile device and the location of the vehicle associated with the user's travel call, and thus the system 200 is efficient and cost effective.

Another aspect of the present application provides methods for locating a building doorway.

Fig. 6 is a flow chart of an exemplary process 600 for locating building doorway according to embodiments of the present application, for example, process 600 may be implemented by system 200, and process 600 may include steps S602-S608 as follows.

In step S602, the system 200 may determine a trajectory of the mobile device based on motion data and a motion model acquired by the mobile device.

It is envisaged that the motion model may comprise at least two sub-models, and each sub-model corresponds to the location of the user carrying the mobile device.

FIG. 7 is a flow diagram of an exemplary process 700 for generating a trajectory for a mobile device according to embodiments of the present application, process 700 may also be performed by system 200 and may include sub-steps S702-S706.

In sub-step S702, the system 200 may receive motion data acquired by a mobile device. In sub-step S704, the system 200 may generate at least two motion vectors using a motion model based on the motion data. In sub-step S706, the system 200 may determine the trajectory of the mobile device by synthesizing the motion vectors.

As described above, when the motion model includes at least two sub-models, the system 200 may select a sub-model based on the motion data and determine a motion vector using the sub-model based on the motion data. For example, when the user places the mobile device in a pocket, the system 200 may determine that the mobile device remains in the pocket and determine the motion vector of the pocket using the sub-model. Thus, the system 200 may determine the trajectory of the mobile device.

In step S604, the system 200 may acquire the th terminal position of the trajectory of the mobile device, so that any position of the trajectory may be located in a later step based on the th terminal position and the trajectory itself.

According to an embodiment of the application, the system 200 uses the location of the vehicle based on the travel call associated with the vehicle and the mobile device to obtain the th terminal location.

In an th example, when the user completes the trip and leaves the vehicle, the location of the vehicle at the destination of the trip may be designated as a th terminal location, which is the starting location of the trajectory of the mobile device from the vehicle to the building.

Furthermore, since the start of the trip is usually close to the building, the building can be identified based on the th terminal location.

It is contemplated that the end position in the th example is a start position of the track and the th end position in the second example is an end position of the track, thus, the th end position obtained at step S604 may be either a start position or an end position of the track.

For example, when the mobile device 102 enters the building, the mobile device 102 may be able to receive WiFi signals of WiFi access points disposed within the building.

Because the system 200 may detect a mobile device entering or leaving the building, the location on the trajectory may be determined as the second terminal location of the trajectory when the mobile device is detected entering or leaving the building, the second terminal location of the trajectory may be further designated as the location of the doorway.

The process 600 may automatically locate a building doorway based on the trajectory of the user's mobile device and the location of the vehicle associated with the user's travel call, and thus the process 600 is efficient and cost effective.

Another aspect of the present application relates to non-transitory computer-readable storage media storing instructions that, when executed by or more processors, perform the methods described above.

Various modifications and variations to the disclosed positioning system and associated methods will be apparent to those skilled in the art. Other embodiments will be apparent to those skilled in the art from consideration of the specification and practice of the system and associated method of the present application.

It is intended that the specification and examples be considered as exemplary only, with a true scope being indicated by the following claims and their equivalents.