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Performance analysis of adaptive algorithms for space-time adaptive processor (STAP) in phased array radar

机译:相控阵雷达时空自适应处理器(STAP)自适应算法的性能分析

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摘要

The most challenging task in phased array radar system is the mitigation or suppression of noise and interferences to enhance the useful signal in radar received signals. Multiple solutions have been proposed by researchers for suppression of unwanted signals or interferences which have highly degraded the overall system performance. Nowadays, space-time adaptive processor (STAP) which is a two-dimensional filtering technique is commonly used in phased array antenna with multiple spatial channels. A detailed study is performed on the well-known adaptive algorithms least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS), to suppress the high state sea clutter by updating the adaptive STAP weight vector. However, RLS is considered as the suitable adaptive algorithm in STAP because of its good convergence rate, but has high computational complexity. To overcome this issue, two variants of RLS (QRD-RLS and Inverse QRD-RLS) are also discussed which reduce the computational complexity of the algorithm. MATLAB simulations are done to verify the performance of LMS and RLS algorithms in terms of accuracy and convergence rate. Finally, hardware implementations of STAP-QRD-RLS and STAP-IQRD-RLS adaptive beamformers are done and evaluated in terms of latency, throughput and efficiency. The selected platform is the Virtex-5 field-programmable gate array.
机译:相控阵雷达系统中最具挑战性的任务是减轻或抑制噪声和干扰,以增强雷达接收信号中的有用信号。研究人员提出了多种解决方案来抑制不需要的信号或干扰,这极高降低了整体系统性能。如今,作为二维滤波技术的时空自适应处理器(STAP)通常用于具有多个空间通道的相位的阵列天线。在众所周知的自适应算法上执行详细的研究,最小均线(LMS),归一化最小平均正方形(NLMS)和递归最小二乘(RLS),以通过更新自适应STAP权重向量来抑制高状态海杂波。然而,由于其良好的收敛速度,RLS被认为是STAP中的合适的自适应算法,但具有高计算复杂性。为了克服这个问题,还讨论了两个RLS(QRD-RLS和逆QRD-RLS)的变体,这降低了算法的计算复杂性。完成MATLAB仿真以验证LMS和RLS算法的性能,以准确性和收敛速度。最后,完成STAP-QRD-RLS和STAP-IQRD-RLS自适应波束形成器的硬件实现,并根据延迟,吞吐量和效率进行评估。所选平台是Virtex-5现场可编程门阵列。

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