http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-107728143-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01S7-417 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01S13-89 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01S13-04 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01S7-41 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01S13-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01S13-89 |
| filingDate | 2017-09-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2021-01-19-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2021-01-19-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-107728143-B |
| titleOfInvention | Target recognition method of radar high-resolution range image based on one-dimensional convolutional neural network |
| abstract | The invention discloses a radar high-resolution range image target recognition method based on one-dimensional convolutional neural network. The high-resolution radar echo of the radar, and then the Q-class high-resolution range imaging data is obtained from the high-resolution radar echoes of Q different radars, and the Q-class high-resolution range imaging data is divided into a training sample set and a test sample set, and then Record the Q-class high-resolution range imaging data as the original data x; according to the original data x, calculate the mean-normalized data x"'; set a one-dimensional convolutional neural network model, use the training sample set and the mean-normalized The one-dimensional convolutional neural network model is constructed from the normalized data x"' to obtain a trained convolutional neural network; the trained convolutional neural network is used for target recognition using the test sample set, and a one-dimensional convolutional neural network is obtained based on the test sample set. Target recognition results of radar high-resolution range profiles with convolutional neural networks. |
| priorityDate | 2017-09-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID28718 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID419513094 |
Total number of triples: 16.