Metric Sensitivity of the Multi-Sensor Information Fusion Process under Instance-Based Learning

摘要

The study investigates the sensitivity of the instance-based-learning (IBL) driven multi-source information fusion process to the underlying distance metric. An audio-visual system for recognition of spoken French vowels is used as an example for this investigation. Three different distance measures, namely, Euclidean, city block and chess board metrics, are employed for this initial foray into metric sensitivity analysis. In this example, the test phase encompasses a broader range of noise environments of the audio signal as compared to the training phase. The system is thus exercised in both trained and untrained noise regimes. Under the untrained regime, interpolation as well as extrapolation or off-nominal scenarios are considered. In the former, the signal to noise ratio in the test phase is within the range used in training phase but does not specifically include it. In the latter, the signal to noise ratio in the test phase is outside the range used in the training phase. It is observed that while both of the single-sensor based decision systems individually are not very sensitive to the choice of the metric, the fused decision system is indeed significantly more sensitive to this choice. The city block metric offers better performance as compared to the other two in the case of the fused audio-visual system across most of the spectrum of noise environments, except for the extreme off-nominal conditions wherein the Euclidean offers slightly better performance. The chess board metric offers the lowest performance across the entire test range. The lack of training in the interpolation scenario has a noticeably strong effect on audio performance under the chess board metric.