Ambiguity reduction of underwater targets in framework of topic modeling
An unsupervised track classification approach based on appropriate discriminative and aggregative features derived from beamformed and normalized matched-filtered data is applied to sonar multistatic tracking and extended to include discretised track velocity and heading rate. A clustering algorithm based on the Latent Dirichlet Allocation model is proposed. It is demonstrated how low-level, highly variable and non-stationary data components can be combined through an increased abstraction level with higher level kinematic tracking features. Improved discrimination of tracks associated with both stationary and moving scatterers is demonstrated.
SourceIn: 18th International Conference on Information Fusion, 6-9 July 2015, Washington DC, USA, pp. 2017-2024.
LePage, Kevin D.