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A Robust Direct Data Domain Approach for STAP

机译:用于STAP的可靠的直接数据域方法

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摘要

In this paper, a novel approach for direct data domain space time adaptive processing (STAP) is presented. As already described in past literature, direct data domain STAP (also known as deterministic STAP) has several advantages compared to traditional stochastic STAP. In particular, being implicitly a single snapshot interference cancellation technique, deterministic STAP generally outperforms stochastic STAP in fast varying interference scenarios. On the other hand, in its classical derivation, target detection performances of deterministic STAP are severely deteriorated in case of uncertainty in the knowledge of exact target parameters as direction of arrival (DOA) and Doppler frequency. To overcome this problem, we propose a robust implementation of deterministic STAP in order to take into account a possible mismatch between the nominal and the actual target parameters. The proposed approach reformulates the deterministic STAP problem in the context of convex problem optimization. A detailed analysis of the maximum acceptable target parameters error is conducted, which ensures the existence of a numerical solution for the convex problem optimization. The proposed robust deterministic approach is defined for both the one dimensional (spatial-only) and the two dimensional (space-time) case. The effectiveness of the proposed approach is shown both in simulated scenarios and by direct application to real data taken from the experimental multichannel radar system PAMIR developed at Fraunhofer FHR.
机译:本文提出了一种新的直接数据域空时自适应处理(STAP)方法。如过去的文献中所述,与传统的随机STAP相比,直接数据域STAP(也称为确定性STAP)具有多个优势。特别地,确定性STAP是隐式的单一快照干扰消除技术,在快速变化的干扰场景中通常要优于随机STAP。另一方面,在经典推导中,如果不确定准确的目标参数(如到达方向(DOA)和多普勒频率)的确定性,确定性STAP的目标检测性能会严重下降。为了克服此问题,我们提出了确定性STAP的可靠实现,以考虑名义参数与实际目标参数之间可能存在的不匹配。所提出的方法在凸问题优化的情况下重新确定了STAP问题。对最大可接受目标参数误差进行了详细分析,这确保了凸问题优化的数值解的存在。针对一维(仅限空间)和二维(时空)情况定义了所提出的鲁棒确定性方法。该方法的有效性在模拟场景中和直接应用于从Fraunhofer FHR开发的实验性多通道雷达系统PAMIR采集的真实数据中均得到了证明。

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