Sensor network performance evaluation in statistical manifolds

Information geometry, as a powerful though complex mathematical tool can provide additional insights in the analysis of sensor measurement. In this paper, the application of information geometry to the analysis of sensor networks is explored in an attempt to gain a better understanding of sensor system issues for target detection and tracking. In particular, the (integrated) Fisher information distance between two targets is used to measure target resolvability in the region covered by the sensor system and is approximately calculated. It is also compared with the Kullback Leibler divergence. The proposed analysis is elucidated via two simple sensor network examples in the context of target tracking. The preliminary analysis results presented in this paper provide evidence that information geometry is able to offer consistent but more comprehensive means to understand and solve sensor network problems which are difficult to deal with via conventional analysis methods.