Abstract
In a sensor detection network, each sensor makes a binary decision based on its own observation, and then communicates its binary decision to a fusion center, where the final decision is made. To implement an optimal fusion center, the performance of each sensor as well as the a priori probabilities of the hypotheses must be known. However, these statistics are usually unknown or may vary with time. In this paper, I will introduce a recursive algorithm that approximates these values on-line and adapts the fusion center. This approach is based on time-averaging of the local decisions and using them to estimate the error probabilities and a priori probabilities of the hypotheses. This method is efficient and its asymptotic convergence is guaranteed.
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© 2008 Springer-Verlag Berlin Heidelberg
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Mirjalily, G. (2008). Adaptive Target Detection in Sensor Networks. In: Sarbazi-Azad, H., Parhami, B., Miremadi, SG., Hessabi, S. (eds) Advances in Computer Science and Engineering. CSICC 2008. Communications in Computer and Information Science, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89985-3_145
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DOI: https://doi.org/10.1007/978-3-540-89985-3_145
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89984-6
Online ISBN: 978-3-540-89985-3
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