An autoregressive (AR) spectrum estimator using the Hopfield neural network (HNN) is introduced. The HNN is designed to minimize the mean squared error between a subject signal and the assumed AR model of the signal. The output of the HNN is the estimated AR coefficients; thus, the spectrum of the signal can be directly obtained in terms of the AR coefficients and the sampling interval. An odd symmetric soft-limiter-type neuron is selected for the HNN rather than the sigmoid or the hard limiter type. The idea is extended for the bearing estimation problem for sonar and radar. A hardware implementation of the model is discussed.
|Number of pages||4|
|Journal||Proceedings - IEEE International Symposium on Circuits and Systems|
|Publication status||Published - 1990 Dec 1|
|Event||1990 IEEE International Symposium on Circuits and Systems Part 4 (of 4) - New Orleans, LA, USA|
Duration: 1990 May 1 → 1990 May 3