Multi-dimensional multiple hypothesis tracking with a Gaussian mixture model to suppress grating lobes.
Tetsutaro YamadaToshihiro ItoHideyuki IzumiSatoru MurayamaYoshihiro SawayamaYasushi ObataPublished in: FUSION (2017)
Keyphrases
- gaussian mixture model
- multi dimensional
- multiple hypothesis tracking
- data association
- mixture model
- em algorithm
- feature vectors
- speaker recognition
- background subtraction
- speaker identification
- maximum likelihood
- expectation maximization
- feature space
- unsupervised learning
- gaussian mixture
- machine learning
- high dimensional
- bayesian information criterion
- feature extraction