Range class estimation for radio frequency devices
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.
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
P
r
_
d
=
-
10
*
β
*
log
10
(
d
d
o
)
+
P
r
_
do
,
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
P
r
_
d
=
-
10
*
β
*
log
10
(
d
d
o
)
+
P
r
_
do
,
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
P
r
_
d
=
-
10
*
β
*
log
10
(
d
d
o
)
+
P
r
_
d
o
,
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.