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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Model-and-Data-Driven Method for Radar Highly Maneuvering Target Detection
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Model-and-Data-Driven Method for Radar Highly Maneuvering Target Detection

机译：用于雷达高机动目标检测的模型和数据驱动方法

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摘要

This article addresses the coherent integration problem for detecting a highly maneuvering radar target with the range migration (RM) and Doppler frequency migration (DFM). Although many research efforts have been devoted toward this problem, how to strike a good balance between the detection performance and the computational complexity is still a challenge yet. In the algorithm, proposed in this article, a neural network is first developed to directly infer the target trajectory from the radar echo, and then the target energy distributed along the inferred trajectory is accumulated via the dechirp technique for detection. Since the proposed algorithm corrects the RM and compensates the DFM via the data-driven and model-driven approaches, respectively, we argue that the proposed algorithm operates in a model-and-data-driven approach. Besides, we fully integrate the domain knowledge into the development of the neural network, and simulation results suggest that this practice helps improve the detection performance of the proposed algorithm. Finally, numerical experiments are provided to show the high detection performance and computational efficiency of the proposed algorithm. Furthermore, we visualize the learned information of the neural network and find that it accords with our domain knowledge, demonstrating the rationality of the neural network's predictions.
机译：本文解决了检测具有范围迁移（RM）和多普勒频率迁移（DFM）的高机动雷达目标的相干积分问题。虽然许多研究努力已经探讨了这个问题，但如何在检测性能和计算复杂性之间取得良好的平衡仍然是一个挑战。在本文中提出的算法中，首先开发一个神经网络以直接从雷达回波推断目标轨迹，然后通过DecHirp技术累积沿推断轨迹分布的目标能量进行检测。由于所提出的算法校正RM并通过数据驱动和模型驱动的方法来补偿DFM，因此我们认为所提出的算法以模型和数据驱动的方法运行。此外，我们将域名知识完全集成到神经网络的发展中，仿真结果表明这种做法有助于提高所提出的算法的检测性能。最后，提供了数值实验以显示所提出的算法的高检测性能和计算效率。此外，我们可以想象神经网络的学到信息，并发现它符合我们的域知识，展示了神经网络预测的合理性。

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