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Spatiotemporal radar target identification using radar cross-section modeling and hidden Markov models

机译：利用雷达横截面建模和隐马尔可夫模型识别时空雷达目标

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摘要

We propose a target identification scheme exploiting the temporal dependency and spatial structure of radar cross-section (RCS) measurements in low-frequency, passive, or long-range surveillance radar systems. We employ target RCS modeling integrated into a hidden Markov model (HMM) with state-duration modeling. Assuming that the radar system ensures a sufficient sampling time, it is possible to use the spatial characteristics of airborne targets, because there is consistency between successively sampled RCS measurements. In addition, to exploit the whole temporal characteristic of the sequence of RCS measurements,which has rarely been considered in the literature, we adopt the HMM and target RCS modeling. To accomplish this task, we accurately develop target RCS models and establish the relationship between the target-sensor orientations, which are the hidden states of the HMM, and the correspondingRCSmeasurements. The proposed target identification scheme, which only uses the sequence of RCS measurements, is demonstrated with simulation results and an analysis for various signal-to-noise ratios and target-fluctuation models.
机译：我们提出了一种目标识别方案，该方案利用了低频，无源或远程监视雷达系统中雷达横截面（RCS）测量的时间依赖性和空间结构。我们将目标RCS建模与状态持续时间建模集成到隐藏的马尔可夫模型（HMM）中。假设雷达系统确保有足够的采样时间，则可以使用机载目标的空间特性，因为连续采样的RCS测量之间存在一致性。另外，为了利用RCS测量序列的整个时间特征，这在文献中很少被考虑，我们采用HMM和目标RCS建模。为了完成此任务，我们准确地开发了目标RCS模型，并建立了目标传感器方向（即HMM的隐藏状态）与相应的RCS测量值之间的关系。通过仿真结果以及对各种信噪比和目标波动模型的分析，演示了仅使用RCS测量序列的拟议目标识别方案。

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