Proc. SPIE. 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII
KEYWORDS: Detection and tracking algorithms, Sensors, Matrices, Digital filtering, Sensor networks, Sensor performance, Monte Carlo methods, Filtering (signal processing), Image information entropy, Network centric warfare
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
. 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.