Control radar target calibration method using improved density-based spatial clustering of applications with noise (DBSCAN) algorithm
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Abstract:
In order to explore the target calibration method of air traffic control radar based on eye movement data, the radar control simulator and eye tracker and other devices were used to build an experimental platform. Eight controller students were recruited to participate in the simulation control experiment and eye movement data were collected. Based on the collected eye movement data, the radar target calibration was performed by using the DBSCAN algorithm. However, it is found that the subjective input parameters will lead to the inability to complete the clustering well. Therefore, we propose to improve the DBSCAN algorithm from the two aspects of adaptive selection of neighborhood values and variable density threshold design based on the K-nearest neighbor algorithm and the variable density threshold setting method. And the adaptive operation of the algorithm is realized. The method of determining the neighborhood value by using the fitting distribution density function extreme point and the amplification factor in the improvement process was verified. And the error of other radar targets is only 8.6% and 10%, indicating that the improved processing method has certain applicability. By comparing the extraction results of different aircraft target areas of interest, it is found that the proposed method of calibrating radar targets has certain accuracy and universality.
