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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Van-Houtte, P. Deegan, K. Khorasani, K. |
| Copyright Year | 1991 |
| Description | Author affiliation: Concordia Univ., Montreal, Que., Canada (Van-Houtte, P.; Deegan, K.; Khorasani, K.) |
| Abstract | The utilization of a two-stage neural network architecture for the detection of targets in a passive, listen-only sonar is discussed. The two-stage network consists of a first-stage Hopfield network to suppress noise, and a second stage using a bidirectional associative memory (BAM) to make the decision as to whether a target has been detected or not. A second architecture using only a single BAM stage is also presented for illustrative purposes. The target is assumed to be emitting a single tone sinusoid as its signature. The system also assumes only white Gaussian noise perturbation to the signal. It is shown that this network structure provides correct detection at a signal-to-noise ratio of -21 dB, a 6 dB improvement in target detection over a similar network using a perceptron in the second stage. Performance is shown to be limited to the size of the Hopfield network, in the first stage, and to the training set applied to it. |
| Starting Page | 1154 |
| Ending Page | 1159 |
| File Size | 261048 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780302273 |
| DOI | 10.1109/IJCNN.1991.170552 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1991-11-18 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Magnesium compounds Sonar detection Associative memory Gaussian noise Object detection Underwater acoustics Frequency Multilayer perceptrons Delay effects |
| Content Type | Text |
| Resource Type | Article |
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