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Using Target RCS when Tracking Multiple Rayleigh Targets

机译：跟踪多个瑞利目标时使用目标RCS

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摘要

Closely-spaced (but resolved) targets pose a challenge for measurement-to-track data association algorithms. Since the Mahalanobis distances between measurements collected on closely-spaced targets and tracks are similar, several elements of the corresponding kinematic measurement-to-track assignment cost matrix are also similar. Lacking any other information upon which to base assignments, it is not surprising that data association algorithms make mistakes. Given this problem many in the target tracking community have speculated that measurement-to-track data association could be improved through the use of features. Since the radar cross section (RCS) naturally occurs with the detection and kinematic measurement and does not require use of high-energy/high-resolution waveforms, it is typically a favored feature for radar systems. To gain insight into its impact on measurement-to-track data association, the benefits of RCS-assisted tracking are analytically assessed in several simple cases. Both the rigorous hypothesis probability approach and several less rigorous ad hoc approaches (which are commonly used in practice) are treated. Results vary between the approaches, but the conclusion is that the benefit of RCS-assisted tracking is limited. In fact applying it blindly in every dwell can actually degrade measurement-to-track data association. For this reason the steps required to incorporate RCS-assisted tracking in a beneficial manner are also identified.
机译：间隔较近（但已分解）的目标给测量到跟踪数据关联算法带来了挑战。由于在紧密间隔的目标上收集的测量值与轨道之间的马氏距离相似，因此相应的运动学测量-轨道分配成本矩阵的几个元素也相似。缺少分配所依据的任何其他信息，数据关联算法会犯错误也就不足为奇了。鉴于此问题，目标跟踪社区中的许多人已经推测可以通过使用功能来改善测量到跟踪数据的关联。由于雷达横截面（RCS）自然随检测和运动学测量而发生，并且不需要使用高能量/高分辨率波形，因此它通常是雷达系统的首选功能。为了深入了解其对测量与跟踪数据关联的影响，在几种简单的情况下，通过分析评估了RCS辅助跟踪的好处。严格的假设概率方法和较不严格的临时方法（通常在实践中使用）都得到了处理。两种方法的结果各不相同，但结论是RCS辅助跟踪的好处是有限的。实际上，盲目地在每个驻留中应用它实际上会降低测量到跟踪数据的关联性。因此，还确定了以有益的方式合并RCS辅助跟踪所需的步骤。

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