We consider the problem of estimating the performance of a system that tracks moving objects on the ground using airborne sensors. Expected Track Life (ETL) is a measure of performance that indicates the ability of a tracker to maintain track for extended periods of time. The most desirable method for computing ETL would involve the use of large sets of real data with accompanying truth. This accurately accounts for sensor artifacts and data characteristics, which are difficult to simulate. However, datasets with these characteristics are difficult to collect because the coverage area of the sensors is limited, the collection time is limited, and the number of objects that can realistically be truthed is also limited. Thus when using real datasets, many tracks are terminated because the objects leave the field of view or the end of the dataset is reached. This induces a bias in the estimation when the ETL is computed directly from the tracks. An alternative to direct ETL computation is the use of Markov-Chain models that use track break statistics to estimate ETL. This method provides unbiased ETL estimates from datasets much shorter than what would be required for direct computation. In this paper we extend previous work in this area and derive an explicit expression of the ETL as a function of track break statistics. An example illustrates the properties and advantages of the method.

Date Published: 17 May 2012
PDF: 10 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 839204 (17 May 2012); doi: 10.1117/12.918879

Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)