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Channel Impulse Response based Fingerprinting Localization

 

Objective

Investigate more accurate indoor localization based on fingerprinting.

Issues

  1. Noise and interference.
  2. EM scattering.

 

Approach
  1. Extract Channel Impulse Response (CIR) features continuously from physical layer.
  2. 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

Issues
  1. Error accumulation with DR techniques.
  2. Costly finger-printing techniques or limited resolution

 

Approach
  1. Pervasiveness of multi-sensor mobile devices for localization with DR technique.
  2. Overcome cumulative DR error with automatic range correction from sparse localization infrastructure.
  3. Augment with map information for greater accuracy.

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Cluster-based Localization in Ad Hoc Networks

 

Objective

Develop a cluster-based localization technique that leverages on cluster structure and availability to limited anchor points.

 

Issues
  1. Generally not possible for all nodes to know their locations.
  2. With known accurate anchors, to what extent can this be used to determine position of other nodes

 

Approach
  1. Nodes organize into clusters.
  2. Some nodes are anchors.
  3. 2-phase location estimation:
  4. Head nodes estimate member nodes’ positions based on uploaded info from member nodes
  5. Estimates shared with cluster heads and member nodes for refinement

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Multimodal Location Sensing Fusion

 

Objective
Develop a scalable system that fuses multiple modalities of location sensing to provide the most accurate and relevant location information.

 

Issues
  1. Disparate indoor and outdoor localization.
  2. Inconsistent location metadata formats.
  3. Inconsistent coordinate systems.
  4. Adaptable context at scale.
Approach
  1. Develop layered approach to location information analysis and modeling.
  2. Adopt a Bayesian filtering approach to extract desired location context based on availability of multiple location inputs.