Neural network approach for direction of arrival estimation

The problem of Direction of Arrival (DOA) estimation of users in mobile communication systems using linear antenna arrays is addressed. Superresolution algorithms, such as Multiple Signal Classification (MUSIC), are used to locate desired as well as cochannel mobile users. However these algorithms require extensive computation and are difficult to implement in real-time. In this paper, the DOA problem is approached as a mapping problem which can be modeled using a suitable artificial neural network trained with input output pairs. A study of a three-layer Radial Basis Function Neural Network (RBFNN) which can learn multiple source direction finding with a six-element array is conducted. RBFNNs were used due to their ability for data interpolation in higher dimensions. The network weights are modified using the normalized cumulative delta rule. The performance of this network is compared to that of the MUSIC algorithm for both uncorrelated and corrected signals. It was found that networks implementing these functions were indeed successful in performing the required task and their performance approached that of the MUSIC algorithm. It is also shown that the RBFNN substantially reduced the CPU time for the DOA estimation computations.