PATENT DOCUMENT

Publication Number: US-9460388-B2
Application Number: US-201313905552-A
Country: US
Kind Code: B2

Title: Range class estimation for radio frequency devices

Abstract:
Implementations are disclosed for obtaining a range state of a device operating in an indoor environment with radio frequency (RF) signal sources. In some implementations, windowed signal measurements obtained from RF signals transmitted by an RF signal source are classified into range classes that are defined by threshold values obtained from a RF signal propagation model. A range class observation is obtained by selecting a range class among a plurality of range classes based on a percentage of a total number of windowed signal measurements that are associated with the range class. The range class observation is provided as input to a state estimator that estimates a range class that accounts for process and/or measurement noise. The output of the state estimator is provided as input to a state machine.

Claims:
What is claimed is: 
     
       1. A method comprising:
 obtaining, at a device, a set of signal measurements based on a radio frequency (RF) signal transmitted by a RF signal source; 
 applying a window function to the set of signal measurements to obtain a subset of signal measurements; 
 obtaining a probability density function for the subset of signal measurements; 
 obtaining a cumulative distribution function of the subset of signal measurements from the probability density function; 
 defining a plurality of range classes based on the cumulative distribution function and an RF signal propagation model, where each range class includes a percentage of the subset of signal measurements; 
 processing the range classes in a specified order until a range class is identified from the plurality of range classes that includes a threshold percentage of signal measurements, where the first range class processed represents a closest distance to the RF signal source; 
 responsive to identifying the range class that includes the threshold percentage of signal measurements, designating the identified range class as a range class observation; and 
 obtaining an estimated range class using the range class observation, where the method is performed by one or more processors. 
 
     
     
       2. The method of  claim 1 , further comprising:
 obtaining a first range state from the estimated range class. 
 
     
     
       3. The method of  claim 2 , further comprising:
 transitioning from the first range state to a second range state based on a number of consecutive adjacent range class estimates. 
 
     
     
       4. The method of  claim 2 , further comprising:
 initiating an action at the device based on the range state. 
 
     
     
       5. The method of  claim 1 , where the threshold values are distances between the device and the RF signal source. 
     
     
       6. The method of  claim 1 , where the threshold values are in power units. 
     
     
       7. The method of  claim 1 , where the probability density function is obtained from a histogram of the subset of signal measurements. 
     
     
       8. The method of  claim 1 , where obtaining an estimated range class using the range class observation, further comprises:
 obtaining the estimated range class using a formulation that accounts for process noise and measurement noise. 
 
     
     
       9. The method of  claim 8 , where a Kalman filter is used to obtain the estimated range class. 
     
     
       10. The method of  claim 1 , further comprising:
 filtering the subset of signal measurements to remove erroneous measurements due to interference. 
 
     
     
       11. The method of  claim 1 , where the signal measurements are received signal strength indicator (RSSI) values. 
     
     
       12. The method of  claim 1 , where the window function provides a window size that is less than or equal to 1 second. 
     
     
       13. The method of  claim 1 , where the RF signal propagation model is given by 
       
         
           
             
               
                 
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         where β is an exponent representing path loss and depends on a specific propagation environment, d o  is a reference distance, P r   _   do  is a reference power received at the reference distance d o , and P r   _   d  is a received power at a distance d from the RF signal source. 
       
     
     
       14. A system comprising:
 one or more processors; 
 memory coupled to the one or more processors and configured to store instructions, which, when executed by the one or more processors, causes the one or more processors to perform operations comprising:
 obtaining a set of signal measurements based on a radio frequency (RF) signal transmitted by a RF signal source; 
 applying a window function to the set of signal measurements to obtain a subset of signal measurements; 
 obtaining a probability density function for the subset of signal measurements; 
 obtaining a cumulative distribution function of the subset of signal measurements from the probability density function; 
 defining a plurality of range classes based on the cumulative distribution function and an RF signal propagation model, where each range class includes a percentage of the subset of signal measurements; 
 processing the range classes in a specified order until a range class is identified from the plurality of range classes that includes a threshold percentage of signal measurements, where the first range class processed represents a closest distance to the RF signal source; 
 
 responsive to identifying the range class that includes the threshold percentage of signal measurements, designating the identified range class as a range class observation; and 
 obtaining an estimated range class using the range class observation. 
 
     
     
       15. The system of  claim 14 , where the memory stores instructions, which, when executed by the one or more processors, causes the one or more processors to perform the operation of:
 obtaining a first range state from the estimated range class. 
 
     
     
       16. The system of  claim 15 , where the memory stores instructions, which, when executed by the one or more processors, causes the one or more processors to perform the operation of:
 transitioning from the first range state to a second range state based on a number of consecutive adjacent range class estimates. 
 
     
     
       17. The system of  claim 15 , where the memory stores instructions, which, when executed by the one or more processors, causes the one or more processors to perform the operation of:
 initiating an action based on the range state. 
 
     
     
       18. The system of  claim 14 , where the threshold values are distances between the device and the RF signal source. 
     
     
       19. The system of  claim 14 , where the threshold values are in power units. 
     
     
       20. The system of  claim 14 , where the probability density function is obtained from a histogram of the subset of signal measurements. 
     
     
       21. The system of  claim 14 , where obtaining an estimated range class using the range class observation, further comprises:
 obtaining the estimated range class using a formulation that accounts for process noise and measurement noise. 
 
     
     
       22. The system of  claim 21 , where a Kalman filter is used to obtain the estimated range class. 
     
     
       23. The system of  claim 14 , where the memory stores instructions, which, when executed by the one or more processors, causes the one or more processors to perform the operation of:
 filtering the subset of signal measurements to remove erroneous measurements due to interference. 
 
     
     
       24. The system of  claim 14 , where the signal measurements are received signal strength indicator (RSSI) values. 
     
     
       25. The system of  claim 14 , where the window function provides a window size that is less than or equal to 1 second. 
     
     
       26. The system of  claim 14 , where the RF signal propagation model is given by 
       
         
           
             
               
                 
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                       10 
                     
                     * 
                     β 
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                     ⁢ 
                     
                         
                     
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                     10 
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                       ( 
                       
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         where β is an exponent representing path loss and depends on a specific propagation environment, d o  is a reference distance, P r   _   do  is a reference power received at the reference distance d o , and P r   _   d  is a received power at a distance d from the RF signal source. 
       
     
     
       27. A non-transitory, computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, causes the one or more processors to perform operations comprising:
 obtaining a set of signal measurements based on a radio frequency (RF) signal transmitted by a RF signal source; 
 applying a window function to the set of signal measurements to obtain a subset of signal measurements; 
 obtaining a probability density function for the subset of signal measurements; 
 obtaining a cumulative distribution function of the subset of signal measurements from the probability density function; 
 defining a plurality of range classes based on the cumulative distribution function and an RF signal propagation model, where each range class includes a percentage of the subset of signal measurements; 
 processing the range classes in a specified order until a range class is identified from the plurality of range classes that includes a threshold percentage of signal measurements, where the first range class processed represents a closest distance to the RF signal source; 
 responsive to identifying the range class that includes the threshold percentage of signal measurements, designating the identified range class as a range class observation; and 
 obtaining an estimated range class using the range class observation. 
 
     
     
       28. The non-transitory, computer-readable storage medium of  claim 27 , further comprising:
 obtaining a first range state from the estimated range class. 
 
     
     
       29. The non-transitory, computer-readable storage medium of  claim 28 , further comprising:
 transitioning from the first range state to a second range state based on a number of consecutive adjacent range class estimates. 
 
     
     
       30. The non-transitory, computer-readable storage medium of  claim 28 , further comprising:
 initiating an action at the device based on the range state. 
 
     
     
       31. The non-transitory, computer-readable storage medium of  claim 27 , where the threshold values are distances between the device and the RF signal source. 
     
     
       32. The non-transitory, computer-readable storage medium of  claim 27 , where the threshold values are in power units. 
     
     
       33. The non-transitory, computer-readable storage medium of  claim 27 , where the probability density function is obtained from a histogram of the subset of signal measurements. 
     
     
       34. The non-transitory, computer-readable storage medium of  claim 27 , where obtaining an estimated range class using the range class observation, further comprises:
 obtaining the estimated range class using a formulation that accounts for process noise and measurement noise. 
 
     
     
       35. The non-transitory, computer-readable storage medium of  claim 34 , where a Kalman filter is used to obtain the estimated range class. 
     
     
       36. The non-transitory, computer-readable storage medium of  claim 27 , further comprising:
 filtering the subset of signal measurements to remove erroneous measurements due to interference. 
 
     
     
       37. The non-transitory, computer-readable storage medium of  claim 27 , where the signal measurements are received signal strength indicator (RSSI) values. 
     
     
       38. The non-transitory, computer-readable storage medium of  claim 27 , where the window function provides a window size that is less than or equal to 1 second. 
     
     
       39. The non-transitory, computer-readable storage medium of  claim 27 , where the RF signal propagation model is given by 
       
         
           
             
               
                 
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                       ( 
                       
                         d 
                         
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               , 
             
           
         
         where β is an exponent representing path loss and depends on a specific propagation environment, d o  is a reference distance, P r   _   do  is a reference power received at the reference distance d o , and P r   _   d  is a received power at a distance d from the RF signal source.

Description:
TECHNICAL FIELD 
     This subject matter is generally related to range estimation for electronic devices. 
     BACKGROUND 
     Estimating range between devices is of particular interest to applications that require two or more devices to be in close proximity to communicate and perform transactions. There are several radio frequency (RF) technologies available that can be used to estimate range. These technologies include but are not limited Wi-Fi, Bluetooth Low Energy (BTLE) and Near Field Communication (NFC). Since these RF technologies were not designed for ranging service only, these RF technologies have parasitic effects (e.g., multipath interference) that limit the ability of these RF technologies to estimate range. 
     SUMMARY 
     Implementations are disclosed for obtaining a range state of a device operating in an indoor environment with RF signal sources. In some implementations, windowed signal measurements obtained from RF signals transmitted by a RF signal source are classified into range classes that are defined by threshold values obtained from a RF signal propagation model. A range class observation is obtained by selecting a range class among a plurality of range classes based on a percentage of a total number of windowed signal measurements that are associated with the range class. The range class observation is provided as input to a state estimator that estimates a range class that accounts for process and/or measurement noise. The output of the state estimator is provided as input to a state machine which outputs a range state that can be used to initiate one or more actions on the device, such as communicating with the RF signal source or other devices associated with the environment. 
     In some implementations, a method comprises: a method comprising: obtaining, at a device, a set of signal measurements based on a radio frequency (RF) signal transmitted by a RF signal source; applying a window function to the set of signal measurements to obtain a subset of signal measurements; obtaining a range class observation based on a RF signal propagation model and the subset of signal measurements; and obtaining an estimated range class using the range class observation. 
     Other implementations are directed to methods, systems and computer-readable mediums. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG. 1  illustrates an example indoor environment. 
         FIG. 2  is a block diagram of a system for estimating range. 
         FIG. 3  is a histogram of RSSI values. 
         FIG. 4  is a histogram illustrating example range classes. 
         FIG. 5  is a state diagram illustrating an example state machine for transitioning among range states. 
         FIG. 6  is a flow diagram of an example process for estimating range classes. 
         FIG. 7  illustrates an example operating environment for a mobile device capable of determining range state. 
         FIG. 8  is a block diagram of example mobile device architecture capable of determining range state. 
     
    
    
     DETAILED DESCRIPTION 
     System Overview 
       FIG. 1  illustrates an example indoor environment  100  where mobile device  102  can estimate ranges R 1 -R 4  to RF signal sources  104   a - 104   d . An example indoor environment  100  can be a building (e.g., retail store). RF signal sources  104   a - 104   d  can be RF beacons (e.g., BTLE beacons) or any other RF signal source capable of generating and transmitting RF signals. Mobile device  102  can be a smartphone, navigation device, wearable computer (e.g., wristwatch), electronic tablet or any other device capable of receiving RF signals. RF signal sources  104   a - 104   d  transmit RF signals that are received by a RF receiver (or transceiver) of mobile device  102 . 
     When mobile device  102  establishes communication with one of RF signal sources  104   a - 104   d , information can be transmitted from the RF signal source to mobile device  102 . Information can include advertisements, coupons, maps, directions, instructions or any other information that can be processed by an application running on mobile device  102 . In some implementations, it is desirable to send information when mobile device  102  is within a certain range of the RF signal source (e.g., 30 centimeters). 
     Mobile device  102  can obtain a range state using RF signal measurements obtained from RF signals transmitted from the RF signal source (e.g., beacon transmissions). One example of an RF signal measurement is a received signal strength indicator (RSSI). RSSI is specified in the IEEE 802.11 specification and is an indication of the power level being received by an antenna. RSSI can be mathematically defined as being approximately the ratio of the power of the received signal and a reference received power (e.g., 1 mW) given by 
                     RSSI   ∝     10   ⁢           ⁢   log   ⁢           ⁢       P   r       P   ref           ,           [   1   ]               
where the higher the RSSI number (or less negative) the stronger the signal. Hereinafter, RSSI values will be expressed in dBm.
 
     In addition to walls and a ceiling, indoor environment  100  can include various furniture, structures, customers and other objects that can reflect RF signals from the RF signal sources, causing multipath interference at the RF receiver (or transceiver) of mobile device  102 . Multipath interference occurs when a RF signal from a RF signal source travels to the RF receiver of mobile device  102  along two or more paths, resulting in constructive and/or destructive interference at the RF receiver. Multipath interference makes range difficult to estimate. 
     The received power P r   _   d  (in dBm) at a distance d from an RF signal source, for an indoor environment with multipath interference can be modeled as 
                       P     r   ⁢           ⁢   _   ⁢           ⁢   d       =         -   10     *   β   *   log   ⁢           ⁢   10   ⁢     (     d     d   o       )       +     P     r   ⁢           ⁢   _   ⁢           ⁢   d   ⁢           ⁢   o           ,           [   2   ]               
where β is an exponent representing path loss, d o  is a reference distance (e.g., 1 meter) and P r   _   do  is the reference power received at reference distance d o  (e.g., 1 mW). The value of depends on the specific propagation environment, such as the type of construction material, architecture and location within the environment (e.g., a building). Lowering the value of β lowers the signal loss. The values of β can range from 1.2 to 8 (e.g., 1.8). Equation [2] gives RSSI in dBm for a distance d in meters. As discussed in reference to  FIG. 4 , Equation [2] can be used to convert distance thresholds for range classes (in meters) into RSSI thresholds (in dBm), so that RSSI values can be assigned to range classes based on RSSI thresholds without converting the RSSI values to distances.
 
       FIG. 2  is a block diagram of a system  200  for estimating range. In some implementations, system  200  includes windowing module  202 , interference filter  204 , range classifier  206 , state estimator  208  and state machine  210 . System  200  can be implemented in software, hardware or a combination of software and hardware. Example architecture for implementing system  200  is described in reference to  FIG. 8 . 
     System  200  is configured to provide a range state that can be used by applications that need to know the distance between a mobile device and an RF signal source, such as a BTLE beacon. In some implementations, range classes include: Immediate, Near, Far and Unknown. More or fewer classes can be used as needed for an application. For example, the Immediate range class can be defined as a range between a mobile device and a RF signal source that is, e.g., 0 to 30 centimeters. The Near range class can be defined as a range between a mobile device and a RF signal source that is, e.g., 30 centimeters to 4 meters. The Far range class can be defined as a range between a mobile device and a RF signal source that is, e.g., 4 to 30 meters. The Unknown range class can be defined as the range between a mobile device and a signal source (e.g., greater than 30 meters). Distance thresholds can separate the range classes. The distance thresholds (e.g., in meters) can be converted to RSSI thresholds in dBm using Equation [2], to enable classification of RSSI values, as described in reference to  FIG. 4 , which shows a range class histogram where the range classes (bins) are separated by RSSI thresholds T R1 -T R4 . 
     Windowing module  202  applies a windowing function to a set of signal measurements obtained by mobile device  102  from RF signals, to provide a subset of signal measurements. In some implementations, the set of signal measurements can be a vector of RSSI values computed using Equation [1]. The size of the window can be selected to ensure that the set of signal measurements collected are wide sense stationary (WSS). For some commercial BTLE beacons, RF signals are transmitted at 10 Hz. If the window size is one second, then the RSSI vector will includes 10 RSSI values. The window type and size can be selected based on the specific requirements of an application. In some implementations the window function can be a rectangular window function. 
     The windowed subset of signal measurements can be processed by interference filter  204 . Interference filter  204  can eliminate signal measurements that exceed minimum and maximum values due to interference caused by, for example, electronic components in the mobile device. Interference filter  204  may be optional. 
     Range classifier  206  takes the subset of signal measurements and assigns them to range classes. The range classes, as described above, can be defined by thresholds T R1 -T R4  determined by the RF signal propagation model of Equation [2]. In implementations where RSSI values are the signal measurements, the RSSI values can be assigned to n bins of an RSSI histogram. For example, if the range of the RSSI values is 0-100 dBm, then there can be 100 bins in the RSSI histogram or 1 bin per dBm. An example RSSI histogram is illustrated in  FIG. 3 . The RSSI histogram can be used to approximate a probability density function (PDF) for the RSSI values. 
     A cumulative distribution function (CDF) can be used to assign the signal measurements to range classes. Example range classes are illustrated in  FIG. 4 . In the example shown, the range classes are separated by threshold values T R1 -T R4 , which in this example are in dBm. In the example shown, after the CDF was applied, 60% of the RSSI values from the RSSI histogram were assigned to the Immediate range class, 30% of the RSSI values were assigned to the Near range class and 10% of the RSSI values were assigned to the Far range class. No RSSI values were assigned to the Unknown range class in this example. 
     Once the RSSI values are assigned to range classes, the range classes are processed in a specified order where the first range class processed represents the closest distance to the RF signal source. In the current example, the specified order is Immediate, Near, Far, Unknown. When, during the processing, it is discovered that a range class has at least X % (e.g., 30%) of the total number of RSSI values in the RSSI histogram, then that class is designated as a range class observation and is provided as input into state estimator  208 . In the example shown and assuming X=30%, the Immediate class has 60% of the total RSSI values and is therefore designated as the range class observation. No further processing need be done in this example. If, however, the Immediate class did not have at least 30% of the total RSSI values, then the Near class would be processed. If the Near class has at least 30% of the RSSI values, the Near class would be the designated range class observation. If not, then the Far class would be processed. If none of the Immediate, Near or Far range classes contain at least 30% of the RSSI values, the designated range class observation is Unknown. In some implementations, Unknown rang class observations are not provided as input to state estimator  208 . 
     In some implementations, state estimator  208  can be implemented using an adaptive filter (e.g., adaptive low pass filter). In other implementations, state estimator  208  can be implemented using an extended Kalman filter (EKF) formulation that includes a time update phase and a measurement update phase as follows: 
     A. Time Update
         1. Propagate state
 
   {circumflex over (x)}     −   k   =Φ {circumflex over (x)}     k-1   +Bū   k-1  
   2. Propagate error covariance
 
 P   k   −   =ΦP   k-1 Φ T   +Q  
       

     B. Measurement Update
         1. Compute Kalman gain
 
 K   k   =P   k   −   H   T ( HP   k   −   H   T   +R ) −1  
       

     2. Update estimate with measurement and Kalman gain
 
   {circumflex over (x)}     k   = {circumflex over (x)}     k   −   +K   k (   z     k   −H {circumflex over (x)}     k   − )
         3. Update error covariance with Kalman gain
 
 P   k =( I−K   k   H ) P   k   −   [3]
       

     Since, in this example, range class is the only state to be estimated, the EKF Equations [3] can be simplified as follows (assuming floating point numbers): 
     1. Propagate range class estimating according to [4]. The range classes are each assigned a value. For example, Immediate=1.0, Near=2.0, Far=3.0 and Unknown=4.0. Using these values, if the range class x k  falls in the range 0.0-1.5, the range class is Immediate, if x k  falls in the range 1.5-2.5, the range class is Near, and if x k  is greater than 2.5, the range class is Far.
 
 x   k   =x   k-1   [4]
 
     2. Propagate error covariance according to [5], where an activity factor (AF) is used to scale system noise q to account for system biases and can be set to a floating point number that is greater than zero.
 
 p   k   =p   k-1   +q·AF   [5]
 
     3. Compute Kalman gain k k  according to [6], where r k  is measurement noise that can be a floating point value (e.g., 0.5) that is determined empirically or mathematically based on the signal propagation model [2]. 
     
       
         
           
             
               
                 
                   
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                     k 
                   
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                       k 
                     
                     
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                           p 
                           k 
                         
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                           r 
                           k 
                         
                       
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     4. Update range class estimate with measurement and Kalman gain according to [7]. The parameter z k  is the range class observation determined by range classifier  206 .
 
 x   k   =x   k   +k   k ( z   k   −x   k )  [7]
 
     5. Update error covariance with Kalman gain according to [8].
 
 p   k =(1 −k   k ) p   k   [8]
 
     Once the updated range class estimate is obtained it can be provided as input into state machine  210 . The process noise q k  and measurement noise r k  can be determined based on the motion context of mobile device  102 . For example, q k , r k  can be adjusted depending on whether the mobile device is stationary or moving. 
       FIG. 5  is a state diagram illustrating state machine  210  for transition between range states. Continuing with the previous example, there are four range states: Immediate  502 , Near  504 , Far  506  and Unknown  508 . The triggers for transitions between the range states are based on a number of consecutive adjacent range class estimates. The output of state machine  210  is one of range states  502 ,  504 ,  506 ,  508 . The range state can be provided to one or more applications running on mobile device  102  to initiate actions on mobile device  102 . 
     The entry point into state machine  210  can be based on the updated range class estimate of Equation [7]. For example, if the first range class estimate determined is Immediate, state machine  210  would start in Immediate state  502 . State machine  210  transitions from Immediate state  502  to Near state  504  if state estimator  208  outputs at least 2 consecutive Near range class estimates. If in Near state  504 , then one Immediate class estimate will cause a transition to Immediate state  502 . If in Near state  504 , then 2 consecutive Far range class estimates will cause a transition to Far state  506 . Generally, the number of n consecutive adjacent range class estimates needed to transition between any two states of state machine  210  can be determined based on performance criteria or application requirements. 
     Example Process 
       FIG. 6  is a flow diagram of an example process  600  for estimating range. Process  600  can be implemented by mobile device architecture  800  described in reference to  FIG. 8 . Process  600  can be triggered when mobile device  102  is operating within indoor environments  100 ,  700  as described in reference to  FIGS. 1 and 7 . 
     In some implementations, process  600  can begin by obtaining a set of RF signal measurements from RF signals transmitted by a RF signal source ( 602 ). In some implementations, the RF signal source can be a BTLE beacon. The set of RF signal measurements can be RSSI values. 
     Process  600  can continue by windowing the set of RF signal measurements to obtain a subset of RF signal measurements ( 604 ). The window function can generate a subset of signal measurements based on the assumed dynamics of the mobile device, so that the measurements are WSS, where the mean and variance do not change over time or position of mobile device  102  in indoor environment  100 . An example window function is a rectangular window function with a size of one second or less. 
     Process  600  can continue by filtering the subset of signal measurements to remove erroneous RF signal measurements due to, for example, electronic components in the mobile device ( 606 ). Step  606  is optional. For example, a mean of the subset of RF signal measurements shall fall within a range defined by a minimum value (e.g., −90 dBm) or a maximum value (e.g., 0 dBm). If the RF signal measurements are RSSI values, then this condition is given by Equation [9], where  RSS  is the mean
 
min&lt;   RSS   &lt;max  [9]
 
     Process  600  can continue by determining a range class observation from the subset (and possibly filtered by step  604 ) of signal measurements ( 608 ). In some implementations, the probability density function is estimated from a histogram of the RSSI values. A CDF can then be used to assign RSSI values to range classes. In some implementations, the range classes can be Immediate, Near, Far and Unknown, as described in reference to  FIG. 4 . The thresholds T R1 -T R4  that define the class boundaries in a range class histogram (See  FIG. 4 ) can be determined using the RF signal propagation model [2]. The range classes can be processed from Immediate to Far until process  600  discovers that a range class has X % of the total signal measurements, at which time the processing stops and the range class with X % of the total signal measurements is the range class observation. 
     Process  600  can continue by estimating the range class based on the range class observation ( 610 ). In some implementations, the range classes are assigned floating point values which are input into a state estimator. For example, Immediate=1.0, Near=2.0, Far=3.0 and Unknown=4.0. The state estimator can be any suitable state estimator or predictor, including a linear or non-linear predictive filter, adaptive filter or an EKF. In some implementations, the state estimator computes an estimated range class while taking into account the effects of process noise and measurement noise (e.g., using an EKF). 
     Process  600  can continue by obtaining a range state from the estimated range class ( 612 ). For example, the estimated range class can be input to a state machine that outputs a range state based on a number of consecutive adjacent range state estimates, as described in reference to  FIG. 5 . 
     The range state resulting from step  612  can be provided to applications through an Application Programming Interface (API). For example, the range state can be used to determine whether the mobile device is in the immediate vicinity of a RF signal source, near a RF signal source or far from a RF signal source and initiate actions based on the range state. For example, if the range state is Immediate, an application running on the mobile device may communicate with a server computer associated with the indoor environment (e.g., a retail store) through the RF signal source or through another communication channel. An identifier of the RF signal source (e.g., a MAC address) and the Immediate state can be used by the server computer to determine the range of the mobile device from a RF signal source and send various information to the mobile device through the RF signal source or other communication channel, such as advertisements, coupons, instructions, maps, audio or video files, command for initiating force feedback on the mobile device (e.g., a command to vibrate) or any other desired action. 
     Example Operating Environment 
       FIG. 7  illustrates an example operating environment for a mobile device. Mobile devices  702   a  and  702   b  can, for example, communicate over one or more wired and/or wireless networks  710  in data communication. For example, a wireless network  712 , e.g., a cellular network, can communicate with a wide area network (WAN)  714 , such as the Internet, by use of a gateway  716 . Likewise, an access device  718 , such as an 802.11x wireless access device, can provide communication access to the wide area network  714 . 
     In some implementations, both voice and data communications can be established over the wireless network  712  and the access device  718 . For example, the mobile device  702   a  can place and receive phone calls (e.g., using VoIP protocols), send and receive e-mail messages (e.g., using POP3 protocol), and retrieve electronic documents and/or streams, such as web pages, photographs, and videos, over the wireless network  712 , gateway  716 , and wide area network  714  (e.g., using TCP/IP or UDP protocols). Likewise, in some implementations, the mobile device  702   b  can place and receive phone calls, send and receive e-mail messages, and retrieve electronic documents over the access device  718  and the wide area network  714 . In some implementations, the mobile device  702   a  or  702   b  can be physically connected to the access device  718  using one or more cables and the access device  718  can be a personal computer. In this configuration, the mobile device  702   a  or  702   b  can be referred to as a “tethered” device. 
     The mobile devices  702   a  and  702   b  can also establish communications by other means. For example, the wireless device  702   a  can communicate with other wireless devices, e.g., other mobile devices  702   a  or  702   b , cell phones, etc., over the wireless network  712 . Likewise, the mobile devices  702   a  and  702   b  can establish peer-to-peer communications  720 , e.g., a personal area network, by use of one or more communication subsystems, such as the RF signal sources  104   a - 104   c  described in reference to  FIG. 1 . Other communication protocols and topologies can also be implemented. 
     The mobile device  702   a  or  702   b  can, for example, communicate with one or more services over the one or more wired and/or wireless networks. For example, navigation service  730  can provide navigation information, e.g., map information, location information, route information, and other information, to the mobile device  702   a  or  702   b.    
     Example Mobile Device Architecture 
       FIG. 8  is a block diagram of example mobile device architecture. Architecture  800  may be implemented in any device capable of implementing the features and processes described in reference to  FIGS. 1-7 , including but not limited to portable computers, smart phones, navigation devices and electronic tablets. 
     Architecture  800  may include memory interface  802 , data processor(s), image processor(s) or central processing unit(s)  804 , and peripherals interface  806 . Memory interface  802 , processor(s)  804  or peripherals interface  806  may be separate components or may be integrated in one or more integrated circuits. One or more communication buses or signal lines may couple the various components. 
     Sensors, devices, and subsystems may be coupled to peripherals interface  806  to facilitate multiple functionalities. For example, motion sensor  810 , light sensor  812 , and proximity sensor  814  may be coupled to peripherals interface  806  to facilitate orientation, lighting, and proximity functions of the device. For example, in some implementations, light sensor  812  may be utilized to facilitate adjusting the brightness of touch surface  846 . In some implementations, motion sensor  810  (e.g., an accelerometer, gyros) may be utilized to detect movement and orientation of the device. Accordingly, display objects or media may be presented according to a detected orientation (e.g., portrait or landscape). 
     Other sensors may also be connected to peripherals interface  806 , such as a temperature sensor, a biometric sensor, or other sensing device, to facilitate related functionalities. 
     Location processor  815  (e.g., GPS receiver, WiFi baseband processor) may be connected to peripherals interface  806  to provide geo-positioning. Electronic magnetometer  816  (e.g., an integrated circuit chip) may also be connected to peripherals interface  806  to provide data that may be used to determine the direction of magnetic North. Thus, electronic magnetometer  816  may be used as an electronic compass. 
     Camera subsystem  820  and an optical sensor  822 , e.g., a charged coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) optical sensor, may be utilized to facilitate camera functions, such as recording photographs and video clips. 
     Communication functions may be facilitated through one or more communication subsystems  824 . Communication subsystem(s)  824  may include one or more wireless communication subsystems. Wireless communication subsystems  824  may include radio frequency receivers and transmitters and/or optical (e.g., infrared) receivers and transmitters. Wired communication system may include a port device, e.g., a Universal Serial Bus (USB) port or some other wired port connection that may be used to establish a wired connection to other computing devices, such as other communication devices, network access devices, a personal computer, a printer, a display screen, or other processing devices capable of receiving or transmitting data. 
     The specific design and implementation of the communication subsystem  824  may depend on the communication network(s) or medium(s) over which the device is intended to operate. For example, a device may include wireless communication subsystems designed to operate over a global system for mobile communications (GSM) network, a GPRS network, an enhanced data GSM environment (EDGE) network, 802.x communication networks (e.g., Wi-Fi, Wi-Max), code division multiple access (CDMA) networks, NFC networks and Bluetooth™ networks. Communication subsystems  824  may include hosting protocols such that the device may be configured as a base station for other wireless devices. As another example, the communication subsystems may allow the device to synchronize with a host device using one or more protocols, such as, for example, the TCP/IP protocol, HTTP protocol, UDP protocol, and any other known protocol. 
     Audio subsystem  826  may be coupled to a speaker  828  and one or more microphones  830  to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and telephony functions. 
     I/O subsystem  840  may include touch controller  842  and/or other input controller(s)  844 . Touch controller  842  may be coupled to a touch surface  846 . Touch surface  846  and touch controller  842  may, for example, detect contact and movement or break thereof using any of a number of touch sensitivity technologies, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch surface  846 . In one implementation, touch surface  846  may display virtual or soft buttons and a virtual keyboard, which may be used as an input/output device by the user. 
     Other input controller(s)  844  may be coupled to other input/control devices  848 , such as one or more buttons, rocker switches, thumb-wheel, infrared port, USB port, and/or a pointer device such as a stylus. The one or more buttons (not shown) may include an up/down button for volume control of speaker  828  and/or microphone  830 . 
     In some implementations, device  800  may present recorded audio and/or video files, such as MP3, AAC, and MPEG files. In some implementations, device  800  may include the functionality of an MP3 player and may include a pin connector for tethering to other devices. Other input/output and control devices may be used. 
     Memory interface  802  may be coupled to memory  850 . Memory  850  may include high-speed random access memory or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices, or flash memory (e.g., NAND, NOR). Memory  850  may store operating system  852 , such as Darwin, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks. Operating system  852  may include instructions for handling basic system services and for performing hardware dependent tasks. In some implementations, operating system  852  may include a kernel (e.g., UNIX kernel). 
     Memory  850  may also store communication instructions  854  to facilitate communicating with one or more additional devices, one or more computers or servers. Communication instructions  854  may also be used to select an operational mode or communication medium for use by the device, based on a geographic location (obtained by the GPS/Navigation instructions  868 ) of the device. Memory  850  may include graphical user interface instructions  856  to facilitate graphic user interface processing, including a touch model for interpreting touch inputs and gestures; sensor processing instructions  858  to facilitate sensor-related processing and functions; phone instructions  860  to facilitate phone-related processes and functions; electronic messaging instructions  862  to facilitate electronic-messaging related processes and functions; web browsing instructions  864  to facilitate web browsing-related processes and functions; media processing instructions  866  to facilitate media processing-related processes and functions; GPS/Navigation instructions  868  to facilitate GPS and navigation-related processes; camera instructions  870  to facilitate camera-related processes and functions; and other instructions  872  for implementing the features and processes described in reference to  FIGS. 1-7 . 
     Each of the above identified instructions and applications may correspond to a set of instructions for performing one or more functions described above. These instructions need not be implemented as separate software programs, procedures, or modules. Memory  850  may include additional instructions or fewer instructions. Furthermore, various functions of the device may be implemented in hardware and/or in software, including in one or more signal processing and/or application specific integrated circuits. 
     The features described may be implemented in digital electronic circuitry or in computer hardware, firmware, software, or in combinations of them. The features may be implemented in a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device, for execution by a programmable processor; and method steps may be performed by a programmable processor executing a program of instructions to perform functions of the described implementations by operating on input data and generating output. 
     The described features may be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. A computer program is a set of instructions that may be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program may be written in any form of programming language (e.g., Objective-C, Java), including compiled or interpreted languages, and it may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. 
     Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, and the sole processor or one of multiple processors or cores, of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer may communicate with mass storage devices for storing data files. These mass storage devices may include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory may be supplemented by, or incorporated in, ASICs (application-specific integrated circuits). 
     To provide for interaction with an author, the features may be implemented on a computer having a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the author and a keyboard and a pointing device such as a mouse or a trackball by which the author may provide input to the computer. 
     The features may be implemented in a computer system that includes a back-end component, such as a data server or that includes a middleware component, such as an application server or an Internet server, or that includes a front-end component, such as a client computer having a graphical user interface or an Internet browser, or any combination of them. The components of the system may be connected by any form or medium of digital data communication such as a communication network. Examples of communication networks include a LAN, a WAN and the computers and networks forming the Internet. 
     The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 
     One or more features or steps of the disclosed embodiments may be implemented using an Application Programming Interface (API). An API may define on or more parameters that are passed between a calling application and other software code (e.g., an operating system, library routine, function) that provides a service, that provides data, or that performs an operation or a computation. 
     The API may be implemented as one or more calls in program code that send or receive one or more parameters through a parameter list or other structure based on a call convention defined in an API specification document. A parameter may be a constant, a key, a data structure, an object, an object class, a variable, a data type, a pointer, an array, a list, or another call. API calls and parameters may be implemented in any programming language. The programming language may define the vocabulary and calling convention that a programmer will employ to access functions supporting the API. 
     In some implementations, an API call may report to an application the capabilities of a device running the application, such as input capability, output capability, processing capability, power capability, communications capability, etc. 
     A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made. The systems and techniques presented herein are also applicable to other electronic text such as electronic newspaper, electronic magazine, electronic documents etc. Elements of one or more implementations may be combined, deleted, modified, or supplemented to form further implementations. As yet another example, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. In addition, other steps may be provided, or steps may be eliminated, from the described flows, and other components may be added to, or removed from, the described systems. Accordingly, other implementations are within the scope of the following claims.

Metadata:
Filing Date: 20130530
Publication Date: 20161004
Grant Date: 20161004
Priority Date: 20130530
Inventors: MARTI LUKAS M.
MA SHANNON M.
KAZEMI PEJMAN LOTFALI
Assignee: APPLE INC
CPC Classifications: [{"code": "G06N5/02", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04B17/318", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04B17/3911", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04B17/26", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04B17/27", "inventive": false, "first": false, "tree": "[]"}, {"code": "G01S11/06", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04B17/318", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04B17/27", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04B17/26", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04B17/318", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04B17/26", "inventive": true, "first": false, "tree": "[]"}, {"code": "G01S11/06", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N5/02", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04B17/3911", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04B17/3911", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04B17/27", "inventive": false, "first": false, "tree": "[]"}, {"code": "G01S11/06", "inventive": true, "first": true, "tree": "[]"}]
Family ID: 51179136