Share Email Print

Proceedings Paper

Generating reliable quality of information (QoI) metrics for target tracking
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

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.

Paper Details

Date Published: 11 May 2009
PDF: 8 pages
Proc. SPIE 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII, 733607 (11 May 2009); doi: 10.1117/12.817315
Show Author Affiliations
Chung Huat J. Tan, Imperial College London (United Kingdom)
DSO National Labs. (Singapore)
Duncan F. Gillies, Imperial College London (United Kingdom)

Published in SPIE Proceedings Vol. 7336:
Signal Processing, Sensor Fusion, and Target Recognition XVIII
Ivan Kadar, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?