Expected track length estimation using track break statistics

Pablo O. Arambel; Lucas I. Finn

doi:10.1117/12.918879

17 May 2012 Expected track length estimation using track break statistics

Pablo O. Arambel, Lucas I. Finn

Proceedings Volume 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI; 839204 (2012) https://doi.org/10.1117/12.918879
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

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.

Citation Download Citation

Pablo O. Arambel and Lucas I. Finn "Expected track length estimation using track break statistics", Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 839204 (17 May 2012); https://doi.org/10.1117/12.918879

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KEYWORDS

Sensors

Statistical analysis

Statistical modeling

Computer simulations

Signal processing

Error analysis

Kinematics

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