Abstract
We propose a RANdom SAmple Consensus(RANSAC) method using signal intensity for the track initiation of small target detection. The use of a low constant false-alarm rate threshold to detect a small radar cross-section target can cause numerous false alarms. Consequently, the optimal initialization of the target can be difficult. To solve this issue, we propose a sample-splitting RANSAC (S-RANSAC) method, which compensates for the accuracy degradation due to the lack of inlier data in the RANSAC. The key idea of our approach is to split the received plot data according to signal intensity. We compared the performance of the S-RANSAC method with those of a logic-based algorithm, the Hough transform, and the RANSAC method. Consequently, the S-RANSAC method demonstrated a better track initiation performance than the other methods under the given scenario.
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