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Channel Impulse Response based Fingerprinting Localization
Objective
Investigate more accurate indoor localization based on fingerprinting.
Issues
- Noise and interference.
- EM scattering.
- Extract Channel Impulse Response (CIR) features continuously from physical layer.
- Derive special Euclidean-type similarity metric to find best match in the database of fingerprints.
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SparseTrack: Dead-reckoning with Sparse Range Corrections
Objective
Develop hybrid localization based on dead-reckoning (DR) with automatic range corrections in sparse reference infrastructure
- Error accumulation with DR techniques.
- Costly finger-printing techniques or limited resolution
- Pervasiveness of multi-sensor mobile devices for localization with DR technique.
- Overcome cumulative DR error with automatic range correction from sparse localization infrastructure.
- Augment with map information for greater accuracy.
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Cluster-based Localization in Ad Hoc Networks
Develop a cluster-based localization technique that leverages on cluster structure and availability to limited anchor points.
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Generally not possible for all nodes to know their locations.
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With known accurate anchors, to what extent can this be used to determine position of other nodes
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Nodes organize into clusters.
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Some nodes are anchors.
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2-phase location estimation:
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Head nodes estimate member nodes’ positions based on uploaded info from member nodes
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Estimates shared with cluster heads and member nodes for refinement
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Multimodal Location Sensing Fusion
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Disparate indoor and outdoor localization.
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Inconsistent location metadata formats.
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Inconsistent coordinate systems.
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Adaptable context at scale.
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Develop layered approach to location information analysis and modeling.
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Adopt a Bayesian filtering approach to extract desired location context based on availability of multiple location inputs.