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On Parameter Mismatch for Hidden Markov Models Applied to Indoor Localization
- Source :
- FUSION
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Hidden Markov Chains (HMCs) and, more recently, Hidden semi-Markov Chains (HsMCs) have been used by several groups of researchers to provide a model for indoor localization. A homogeneous HMC is completely determined by the state initial probability vector and the state transition probability matrix. This is also true for the HsMC provided the state duration probability is given. These parameters are often chosen heuristically but when sufficient measurement training data are available, they can be learned using the well-known Baum-Welch algorithm. Given the model parameters, approaches such as the forward-only algorithm, the forward-backwards algorithm and the Viterbi algorithm can be applied for state sequence inference under the HMC/HsMC framework. In indoor localization applications, there is often insufficient prior information to specify such parameters in advance of the application and they have to be learned from limited amounts of training data. In this paper, we endeavour to evaluate the parameter learning accuracy of the Baum-Welch algorithm using varying amounts of training data, and evaluate the influence of applying inaccurate model parameters on these typical state estimation algorithms under both the HMC and HsMC frameworks. All of the evaluations are based on received signal strength (RSS) for application to indoor localization.
- Subjects :
- Training set
Computer science
010401 analytical chemistry
Inference
02 engineering and technology
Viterbi algorithm
01 natural sciences
Probability vector
0104 chemical sciences
Matrix (mathematics)
symbols.namesake
0202 electrical engineering, electronic engineering, information engineering
symbols
020201 artificial intelligence & image processing
State (computer science)
Hidden Markov model
Algorithm
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 IEEE 23rd International Conference on Information Fusion (FUSION)
- Accession number :
- edsair.doi...........3798abb47c613ca7c81e4c59d75a22b8