Mobile-based positioning using assistance data provided by onboard micro-BSA
This disclosure provides systems, methods and apparatuses for classifying traffic flow using a plurality of learning machines arranged in multiple hierarchical levels. A first learning machine may classify a first portion of the input stream as malicious based on a match with first classification rules, and a second learning machine may classify at least part of the first portion of the input stream as malicious based on a match with second classification rules. The at least part of the first portion of the input stream may be classified as malicious based on the matches in the first and second learning machines.
1. A method performed in a mobile device for estimating a position of the mobile device, the method comprising:
determining a first position estimate for the mobile device based upon time difference of arrival (TDOA) measurements associated with at least a subset of a first plurality of cells, the TDOA measurements corresponding to a first TDOA measurement vector;
determining observed time difference of arrival (OTDOA) assistance data corresponding to the subset of the first plurality of cells, the OTDOA assistance data being stored within a micro-BSA within a memory of the mobile device;
determining a quality of the first position estimate based on a first TDOA residual error vector corresponding to the first position estimate;
detecting a bad cell included within the subset of the first plurality of cells based at least in part on the first TDOA residual error vector;
constructing a second TDOA measurement vector by removing the TDOA measurements associated with the bad cell from the first TDOA measurement vector; and
determining a quality of the second position estimate for the mobile device based at least in part on the second TDOA measurement vector.
2. The method of claim 1 , wherein determining the quality of the second position estimate includes calculating a second TDOA residual error vector using the second position estimate.
3. The method of claim 2 , further comprising: detecting a presence of any additional bad cells based on an evaluation of the second TDOA residual error vector.
4. The method of claim 1 , wherein determining the quality of the first position estimate includes determining whether at least a minimum number of cells are included within the first TDOA measurement vector.
5. The method of claim 1 , wherein determining the quality of the first position estimate includes determining a first geometric dilution of precision (GDOP) between the first position estimate and locations of the cells in the first TDOA measurement vector.
6. The method of claim 5 , further comprising:
determining that the first GDOP exceeds a threshold.
7. The method of claim 6 , further comprising:
indicating an error condition.
8. The method of claim 6 , further comprising:
determining the position of the mobile device without hyperbolic TDOA positioning.
9. A mobile device, comprising:
a processor; and
a memory storing program code that, when executed by the processor, causes the mobile device to perform operations including:
determining a first position estimate for the mobile device based upon time difference of arrival (TDOA) measurements associated with at least a subset of a first plurality of cells, the TDOA measurements corresponding to a first TDOA measurement vector;
determining observed time difference of arrival (OTDOA) assistance data corresponding to the subset of the first plurality of cells, the OTDOA assistance data being stored within a micro-BSA within a memory of the mobile device;
determining a quality of the first position estimate based on a first TDOA residual error vector corresponding to the first position estimate;
detecting a bad cell included within the subset of the first plurality of cells based at least in part on the first TDOA residual error vector;
constructing a second TDOA measurement vector by removing the TDOA measurements associated with the bad cell from the first TDOA measurement vector; and
determining a quality of the second position estimate for the mobile device based at least in part on the second TDOA measurement vector.
10. The mobile device of claim 9 , wherein determining the quality of the second position estimate includes calculating a second TDOA residual error vector using the second position estimate.
11. The mobile device of claim 10 , wherein execution of the program code causes the mobile device to perform operations further comprising:
detecting a presence of any additional bad cells based on an evaluation of the second TDOA residual error vector.
12. The mobile device of claim 10 , wherein determining the quality of the first position estimate includes determining whether at least a minimum number of cells are included within the first TDOA measurement vector.
13. The mobile device of claim 9 , wherein determining the quality of the first position estimate includes determining a first geometric dilution of precision (GDOP) between the first position estimate and locations of the cells in the first TDOA measurement vector.
14. The mobile device of claim 13 , wherein execution of the program code causes the mobile device to perform operations further comprising:
determining that the first GDOP exceeds a threshold.
15. The mobile device of claim 14 , wherein execution of the program code causes the mobile device to perform operations further comprising:
indicating an error condition.
16. The mobile device of claim 14 , wherein execution of the program code causes the mobile device to perform operations further comprising:
determining the position of the mobile device without hyperbolic TDOA positioning.