Unsupervised ICA neural networks applied to reticle optical trackers

Ivica Kopriva; Harold H. Szu

doi:10.1117/12.381677

5 April 2000 Unsupervised ICA neural networks applied to reticle optical trackers

Ivica Kopriva, Harold H. Szu

Proceedings Volume 4056, Wavelet Applications VII; (2000) https://doi.org/10.1117/12.381677
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

Reticle systems are considered to be the classical approach for estimating the position of a target in a considered field of view an are widely used in IR seekers. Due to the simplicity and low cost, since only a few detectors are used, reticle seekers are still in use and are subject of further research. However, the major disadvantage of reticle trackers has been proven to be sensitivity on the IR countermeasures such as flares and jammers. When redesigned adequately they produce output signals that are linear convolutive combinations of the reticle transmission functions that are considered as the source signals in the context of the Independent Component Analysis (ICA) theory. Each function corresponds with single optical source position. That enables ICA neural network to be applied on the optical tracker output signals giving on its outputs recovered reticle transmission functions. Position of each optical source is obtained by applying appropriate demodulation method on the recovered source signals. The three conditions necessary for the ICA theory to work are shown to be fulfilled in principle for any kind of the reticle geometry.

Citation Download Citation

Ivica Kopriva and Harold H. Szu "Unsupervised ICA neural networks applied to reticle optical trackers", Proc. SPIE 4056, Wavelet Applications VII, (5 April 2000); https://doi.org/10.1117/12.381677

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KEYWORDS

Reticles

Independent component analysis

Optical tracking

Frequency modulation

Fermium

Sensors

Modulation

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