  We discuss a new neural network-based direction of arrival estimation scheme
that tackles the estimation task as a multidimensional classification problem.
The proposed estimator uses a classification chain with as many stages as the
number of sources. Each stage is a multiclass classification network that
estimates the position of one of the sources. This approach can be interpreted
as the approximation of a successive evaluation of the maximum a posteriori
estimator. By means of simulations for fully sampled antenna arrays and systems
with subarray sampling, we show that it is able to outperform existing
estimation techniques in terms of accuracy, while maintaining a very low
computational complexity.
