Method and apparatus for constructing wireless positioning feature library
AssigneeHuawei Technologies Co., Ltd.
InventorsXiaofeng Hu, Jiehua Chen
Life of this patent
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Abstract
A method for constructing a wireless positioning feature library includes: obtaining a wireless positioning training data set, where the wireless training data set includes a plurality of pieces of training data; performing clustering and merging on the plurality of pieces of training data one by one; and determining wireless positioning feature data based on at least one piece of sample data obtained after the merging.
Claims20
A method for constructing a wireless positioning feature library, comprising:
- obtaining, by an apparatus, a wireless positioning training data set, wherein the wireless positioning training data set comprises a plurality of pieces of training data;
- performing, by the apparatus, clustering and merging on the plurality of pieces of training data one by one, to obtain at least one new piece of sample data;
- and
- determining, by the apparatus, wireless positioning feature data based on the at least one new piece of sample data;
- wherein performing clustering and merging on a piece of training data A of the plurality of pieces of training data comprises:
- determining, from at least one piece of sample data of a sample data set, sample data that falls within a preset range of the piece of training data A;
- determining, from the sample data that falls within the preset range of the piece of training data A, sample data B with highest similarity to the piece of training data A;
- merging the piece of training data A and the sample data B to form new sample data C;
- and
- replacing the sample data B with the new sample data C in the sample data set.
An apparatus for constructing a wireless positioning feature library, comprising:
- a transceiver;
- at least one processor, and
- a non-transitory computer-readable storage medium having processor-executable instructions stored thereon;
- wherein the processor-executable instructions, when executed by the at least one processor, facilitate:
- obtaining, via the transceiver, a wireless positioning training data set, wherein the wireless positioning training data set comprises a plurality of pieces of training data;
- performing clustering and merging on the plurality of pieces of training data one by one, to obtain at least one new piece of sample data;
- and
- determining wireless positioning feature data based on the at least one new piece of sample data;
- wherein performing clustering and merging on a piece of training data A of the plurality of pieces of training data comprises:
- determining, from at least one piece of sample data of a sample data set, sample data that falls within a preset range of the piece of training data A;
- determining, from the sample data that falls within the preset range of the piece of training data A, sample data B with highest similarity to the piece of training data A;
- merging the piece of training data A and the sample data B to form new sample data C;
- and
- replacing the sample data B with the new sample data C in the sample data set.
A non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein the processor-executable instructions, when executed, facilitate:
- obtaining a wireless positioning training data set, wherein the wireless positioning training data set comprises a plurality of pieces of training data;
- performing clustering and merging on the plurality of pieces of training data one by one, to obtain at least one new piece of sample data;
- and
- determining wireless positioning feature data based on the at least one new piece of sample data;
- wherein performing clustering and merging on a piece of training data A in the plurality of pieces of training data comprises:
- determining, from at least one piece of sample data of a sample data set, sample data that falls within a preset range of the piece of training data A;
- determining, from the sample data that falls within the preset range of the piece of training data A, sample data B with highest similarity to the piece of training data A;
- merging the piece of training data A and the sample data B to form new sample data C;
- and
- replacing the sample data B with the new sample data C in the sample data set.
Dependencies are inferred from the claim text.