Visible to the public Vessel track correlation and association using fuzzy logic and Echo State Networks

TitleVessel track correlation and association using fuzzy logic and Echo State Networks
Publication TypeConference Paper
Year of Publication2014
AuthorsHang Shao, Japkowicz, N., Abielmona, R., Falcon, R.
Conference NameEvolutionary Computation (CEC), 2014 IEEE Congress on
Date PublishedJuly
KeywordsComputational Intelligence, computational-intelligence-based methods, Correlation, correlation methods, data fusion, Defence and Security, defense community, echo state networks, fuzzy c-means clustering, fuzzy k-nearest neighbours, Fuzzy logic, fuzzy set theory, Kalman filter, Kalman filters, Marine vehicles, Maritime Domain Awareness, maritime vessels, Mathematical model, moving object tracking, naval engineering computing, Neural networks, object tracking, pattern clustering, Radar tracking, recurrent neural nets, recurrent neural network, Recurrent neural networks, sensing modality, Sensors, target tracking, Track Association, Track Correlation, Vectors, vessel track association, vessel track correlation

Tracking moving objects is a task of the utmost importance to the defence community. As this task requires high accuracy, rather than employing a single detector, it has become common to use multiple ones. In such cases, the tracks produced by these detectors need to be correlated (if they belong to the same sensing modality) or associated (if they were produced by different sensing modalities). In this work, we introduce Computational-Intelligence-based methods for correlating and associating various contacts and tracks pertaining to maritime vessels in an area of interest. Fuzzy k-Nearest Neighbours will be used to conduct track correlation and Fuzzy C-Means clustering will be applied for association. In that way, the uncertainty of the track correlation and association is handled through fuzzy logic. To better model the state of the moving target, the traditional Kalman Filter will be extended using an Echo State Network. Experimental results on five different types of sensing systems will be discussed to justify the choices made in the development of our approach. In particular, we will demonstrate the judiciousness of using Fuzzy k-Nearest Neighbours and Fuzzy C-Means on our tracking system and show how the extension of the traditional Kalman Filter by a recurrent neural network is superior to its extension by other methods.

Citation Key6900231