  In target tracking, the estimation of an unknown weaving target frequency is
crucial for improving the miss distance. The estimation process is commonly
carried out in a Kalman framework. The objective of this paper is to examine
the potential of using neural networks in target tracking applications. To that
end, we propose estimating the weaving frequency using deep neural networks,
instead of classical Kalman framework based estimation. Particularly, we focus
on the case where a set of possible constant target frequencies is known.
Several neural network architectures, requiring low computational resources
were designed to estimate the unknown frequency out of the known set of
frequencies. The proposed approach performance is compared with the multiple
model adaptive estimation algorithm. Simulation results show that in the
examined scenarios, deep neural network outperforms multiple model adaptive
estimation in terms of accuracy and the amount of required measurements to
convergence.
