Title :
Application of neural networks to bearing estimation
Author :
Arslan, Guner ; Gurgen, Fikert ; Sakarya, F. Ayhan
Author_Institution :
Dept. of Electron. & Commun. Eng., Yildiz Univ., Istanbul, Turkey
Abstract :
This study presents an application of a feedforward neural network (NN) structure to the bearing estimation problem. Using N snapshots from M sensors, the NN estimates the sensor-to-sensor propagation delays, which yield the far-field source location. The proposed network has only one output, which is the direction-of-arrival (DOA) angle. Thus, the network does not require any preprocessing. The NN buffers the sensor data, treats them as multidimensional delayed patterns and gives the location of a sinusoidal signal source in a noisy environment as output. Networks with various hidden nodes are tried with various sensor and snapshot numbers to find the best performance network structure. The effect of intersensor spacing on the performance is investigated. Using the best performance giving structure, the network is trained with various signal to noise ratios (SNRs) and then tested for various SNR levels
Keywords :
delays; direction-of-arrival estimation; feedforward neural nets; speaker recognition; bearing estimation; best performance network structure; direction-of-arrival angle; far-field source location; feedforward neural network; intersensor spacing; multidimensional delayed patterns; sensor-to-sensor propagation delays; signal to noise ratios; sinusoidal signal source; snapshot numbers; Delay estimation; Direction of arrival estimation; Feedforward neural networks; Multidimensional systems; Neural networks; Position measurement; Propagation delay; Signal to noise ratio; Working environment noise; Yield estimation;
Conference_Titel :
Electronics, Circuits, and Systems, 1996. ICECS '96., Proceedings of the Third IEEE International Conference on
Conference_Location :
Rodos
Print_ISBN :
0-7803-3650-X
DOI :
10.1109/ICECS.1996.584445