Title :
ANN for real-time identification of radar objectives
Author :
Bin Song ; Gong Long ; Junma Fu
Author_Institution :
Dept. of Inf. & Control Eng., Xi´an Jiaotong Univ., Shaanxi, China
Abstract :
In order to complete the real-time identification of radar objectives with reliability, a mixed ANN (artificial neural network) that is made up of a function-link network and a modified BP (back propagation) network was introduced. The function-link network may increase the estimation speed, while the modified BP network that fully uses a parameter-estimating sequence ensures the reliability of identification. The experiment has been completed with the AST286 computer. The results show that this mixed network, which both ensures real time and realizes high reliability through a combined decision, is an effective way to accomplish the automatic and real-time identification of radar objectives.<>
Keywords :
backpropagation; neural nets; parameter estimation; radar equipment; real-time systems; ANN; AST286 computer; artificial neural network; function-link network; modified backpropagation network; parameter-estimating sequence; radar objectives; real-time identification; reliability; Anisotropic magnetoresistance; Artificial neural networks; Control engineering; Data mining; Maximum likelihood estimation; Parameter estimation; Polarization; Radar applications; Shape; Smoothing methods;
Conference_Titel :
Antennas and Propagation Society International Symposium, 1992. AP-S. 1992 Digest. Held in Conjuction with: URSI Radio Science Meeting and Nuclear EMP Meeting., IEEE
Conference_Location :
Chicago, IL, USA
Print_ISBN :
0-7803-0730-5
DOI :
10.1109/APS.1992.221397