Radar Track Classification Method Based on High-Dimensional Computing
A radar track classification method based on hyper-dimensional computing is proposed aiming at the problems of large computation,poor interpretability,and difficult model updating in the current ra-dar track classification.Through the super-dimensional coding of radar track data,attributes and sample values are mapped to their respective super-dimensional vectors.Then all kinds of samples are processed to obtain the category super-dimensional vector set using simple algorithms and training retraining.The similarity measure between the super-dimensional vectors can categorize the test samples.The experimen-tal results show that the recognition time of this method is 1.28s and the recognition accuracy is 91.78%,which significantly reduces the operation time under the acceptable recognition rate level.
万方数据
维普
