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Robust Estimation for Ship-Borne Radar Detecting Biases

机译:舰载雷达探测偏差的鲁棒估计

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According to the ship-borne radar data processing, the problem of registration of multiple ship-borne 3D radars using common targets tracked by the radars was examined, and a real-time registration algorithm was developed to absolutely align the radar equivalent biases. First, we explicitly avoid the individual biases and instead use equivalent biases modeling the four main class biases, which leads to a highly nonlinear bias model that contains 12 unknown parameters. Then, we use the singular value decomposition (SVD) within least-squares estimator to automatically handle the issue of parameter observability. Finally, according to simulation scene, we demonstrate that our algorithm can improve track accuracy, especially for ship-borne radar.
机译:通过对舰载雷达数据的处理,研究了利用雷达跟踪的共同目标对多个舰载3D雷达进行配准的问题,并开发了一种实时配准算法,以完全对准雷达的等效偏差。首先,我们明确避免使用单个偏差,而是使用对四个主要类别偏差建模的等效偏差,从而导致高度非线性的偏差模型,其中包含12个未知参数。然后,我们在最小二乘估计器中使用奇异值分解(SVD)来自动处理参数可观察性问题。最后,根据仿真场景,证明了我们的算法可以提高跟踪精度,特别是对于舰载雷达。

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