Mobile-based positioning using assistance data provided by onboard micro-BSA
A method for estimating position of a mobile device which includes receiving, from a network server, observed time difference of arrival (OTDOA) assistance data for a first plurality of cells from a base station almanac (BSA) accessible to the network server. The OTDOA assistance data is stored, within a memory of the mobile device, as a first micro-BSA. A position estimate for the mobile device is determined based upon time difference of arrival (TDOA) measurements associated with an initial subset of the first plurality of cells and initial OTDOA assistance data corresponding to the initial subset of the first plurality of cells. The initial OTDOA assistance data may be generated by the micro-BSA based upon an initial seed estimate.
1. A method performed in a mobile device, the method comprising:
receiving, from a base station almanac (BSA) associated with a network server, observed time difference of arrival (OTDOA) assistance data for a first plurality of cells;
storing, within a memory of the mobile device, the OTDOA assistance data as a first micro-BSA;
determining a first position estimate for the mobile device based on time difference of arrival (TDOA) measurements associated with a subset of the first plurality of cells and initial OTDOA assistance data corresponding to the subset of the first plurality of cells;
predicting an expected route to be traveled by the mobile device based at least in part on the first position estimate;
receiving, from the BSA associated with the network server, OTDOA assistance data for a second plurality of cells, the second plurality of cells being proximate to the expected route to be traveled by the mobile device and including at least one cell not included within the first plurality of cells; and
determining a second position estimate based at least in part on second OTDOA assistance data corresponding to a subset of the second plurality of cells.
2. The method of claim 1 , wherein the predicting includes detecting Doppler shifts in frequencies of signals received by the mobile device.
3. The method of claim 1 , wherein the predicting includes determining the mobile device is traveling on a roadway.
4. The method of claim 3 , wherein the predicting further includes extracting route information from a map including the roadway.
5. The method of claim 1 , further including selecting the second plurality of cells based upon pattern recognition information including information relating to cells proximate the expected route previously used by the mobile device for generating prior position estimates.
6. The method of claim 5 , wherein the pattern recognition information is generated by an artificial intelligence (AI) management module associated with the network server.
7. The method of claim 1 , wherein the expected route is predicted by an artificial intelligence (AI) management module associated with the mobile device.
8. A method, comprising:
sending, from a base station almanac (BSA) associated with a network server, observed time difference of arrival (OTDOA) assistance data for a first plurality of cells to a mobile device, the OTDOA assistance data being stored, within a memory of the mobile device, as a first micro-BSA;
receiving, from the mobile device, a first position estimate for the mobile device based on time difference of arrival (TDOA) measurements associated with a subset of the first plurality of cells and initial OTDOA assistance data corresponding to the subset of the first plurality of cells;
predicting an expected route to be traveled by the mobile device based at least in part on the first position estimate;
sending, from the BSA associated with the network server, observed time difference of arrival (OTDOA) assistance data for a second plurality of cells, the second plurality of cells being proximate to the expected route to be traveled by the mobile device and including at least one cell not included within the first plurality of cells; and
receiving, from the mobile device, a second position estimate based at least in part on second OTDOA assistance data corresponding to a subset of the second plurality of cells.
9. The method of claim 8 , wherein the predicting includes detecting Doppler shifts in frequencies of signals received by the mobile device.
10. The method of claim 8 , wherein the predicting includes determining the mobile device is traveling on a roadway.
11. The method of claim 10 , wherein the predicting further includes extracting route information from a map including the roadway.
12. The method of claim 8 , further including selecting the second plurality of cells based upon pattern recognition information including information relating to cells proximate the expected route previously used by the mobile device for generating prior position estimates.
13. The method of claim 12 , wherein the pattern recognition information is generated by an artificial intelligence (AI) management module associated with the network server.
14. The method of claim 8 , wherein the expected route is predicted by an artificial intelligence (AI) management module associated with the network server.