Evaluating the influence of parameter variations on multi-sensor tracking
conference paper
In this paper wȩ evaluate the influence of variations in the input parameters on the output of a multi-sensor tracking algorithm, using simulated data. The tracking algorithm is a classical Kalman filter using a Probabilistic Data Association. The input to the tracker consists of contact files, each file containing all contacts identified for a specific per source / receiver / ping triplet. The input parameters that are varied are: 1) detection threshold used to identify the contacts, 2) ping repetition rate, 3) amplitude of contact position errors, 4) number of sensors used, 5) target signal-to-noise ratio, 6) relative ping time of sensors, and 7) waveform. A 'standard' set of tracker performance metrics is used to evaluate the tracker output and to look for trends in this output versus parameter values.
TNO Identifier
222412
Source title
Proceedings 11th International Conference on Information Fusion, Fusion 2008, June 30 - july 03, 2008, Colgne, Germany
Pages
1953-1960
Files
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