Generating reliable quality of information (QoI) metrics for target tracking
Chung Huat Tan, Duncan Fyfe Gillies · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Recently considerable research has been undertaken into estimating the quality of information (QoI) delivered by military sensor networks. QoI essentially estimates the probability that the information available from the network is correct. Knowledge of the QoI would clearly be of great use to decision makers using a network. An important class of sensors, that provide inputs to networks in real-life, are concerned with target tracking. Assessing the tracking performance of these sensors is an essential component in estimating the QoI of the whole network. We have investigated three potential QoI metrics for estimating the dynamic target tracking performance of systems based on some state estimation algorithms. We have tested them on different scenarios with varying degrees of tracking difficulty. We performed experiments on simulated data so that we have a ground truth against which to assess the performance of each metric. Our measure of ground truth is the Euclidean distance between the estimated position and the true position. Recently researchers have suggested using the entropy of the covariance matrix as a metric of QoI [1][2]. Two of our metrics were based on this approach, the first being the entropy of the co-variance matrix relative to an ideal distribution, and the second is the information gain at each update of the covariance matrix. The third metric was calculated by smoothing the residual likelihood value at each new measurement point, similar to the model update likelihood function in an IMM filter. Our experiment results show that reliable QoI metrics cannot be formulated by using solely the covariance matrices. In other words it is possible that a covariance matrix can have high information content, while the position estimate is wrong. On the other hand the smoothed residual likelihood does correlate well with tracking performance, and can be measured without knowledge of the true target position.