• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.26 no.9
• /
• pp.806-813
• /
• 2015

In order to satisfy several conditions with respect to size, weight, and costs, automotive radars use an antenna consisting of a small number of receiving channels. If RELAX technique is applied to the automotive radars, angles of targets located in antenna beam can be estimated as well as the number of the targets. However, a small number of receiving channels in the antenna leads to inaccurate spectral estimation in angle domain, which in turn degrades performance of RELAX technique. Therefore, in this study, root-MUSIC technique coupled with MDL criterion is introduced to decide accurate angles of targets in antenna beam. In simulations, we show superior performance of proposed scheme using simulation results when three point targets are located in antenna beam.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.3
• /
• pp.139-147
• /
• 2009

The memory based reasoning just stores in the memory in the form of the training pattern of the representative pattern. And it classifies through the distance calculation with the test pattern. Because it uses the techniques which stores the training pattern whole in the memory or in which it replaces training patterns with the representative pattern. Due to this, the memory in which it is a lot for the other machine learning techniques is required. And as the moreover stored training pattern increases, the time required for a classification is very much required. In this paper, We propose the EAS(Evaluation And Selection) algorithm in order to minimize memory usage and to improve classification performance. After partitioning the training space, this evaluates each partitioned space as MDL and PM method. The partitioned space in which the evaluation result is most excellent makes into the representative pattern. Remainder partitioned spaces again partitions and repeat the evaluation. We verify the performance of Proposed algorithm using benchmark data sets from UCI Machine Learning Repository.