A New Approach for Moving Target Detection using Bartlett Method for Spectral Estimation

F. Ahmed, K. Elbarbary, A. Elbardawiny
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引用次数: 4

Abstract

Moving Target Detection (MTD) is an automated radar signal and data processing system, which is designed to improve the performance of radar systems in the presence of various forms of clutter. Consequently, it provides high probability of detection (Pd) for an acceptable probability of false alarm (Pia). It employs coherent, linear Doppler filtering, adaptive thresholding and a fine ground clutter map to reject ground clutter, rain clutter, birds, and interference. The current MTDs relay on the Fast Fourier Transform (FFT) of the total received data sequence to estimate the clutter power spectrum and consequently reduce or remove its effect on the detection performance. Direct FFT leads to high sidelobes level and requires a significant large computation time. The high level sidelobes increases the false alarm probability at the output of the Doppler filters bank. A solution for reducing the effect of spectral sidelobes is the utilization of window functions. However, this solution leads to widening the main spectral lobe and reduce the Doppler resolution. Also it increases the hardware complexity of the system. In the present work, Bartlett method for spectral estimation which depends on dividing the received data sequence into a number, K, of nonoverlapping segments and averaging the calculated FFT for each segment over K, is applied in the MTD instead of the direct FFT for typical ground based radar. The proposed method enhances the target detection capabilities, by providing higher detection probabilities, lower false alarm rates and an additional gain of 7-10 dB in the improvement factor, in the presence of ground and weather clutter, compared to the traditional one. This is because the sidelobe levels obtained are very small in magnitude. This in turns facilitates the realization of the Doppler filters bank without using additional weighting. The obtained performance can be achieved by applying the direct FFT with weighting function to the total received data sequence, which leads to a more hardware complexity and long time calculations compared to the proposed method. Computer simulation results are presented to support the superiority of the proposed technique. * Egyptian Armed Forces Proceeding of the 11-th ASA7 Conference, 17-19 May 2005 RR-03 910
一种基于Bartlett方法的运动目标检测新方法
运动目标检测(MTD)是一种自动化雷达信号和数据处理系统,旨在提高雷达系统在各种形式杂波存在下的性能。因此,它为可接受的虚警概率(Pia)提供了高检测概率(Pd)。它采用相干线性多普勒滤波、自适应阈值和精细地杂波图来抑制地杂波、雨杂波、鸟杂波和干扰。目前的MTDs继电器对接收到的总数据序列进行快速傅里叶变换(FFT)来估计杂波功率谱,从而减少或消除其对检测性能的影响。直接FFT导致高副瓣电平,需要大量的计算时间。高电平旁瓣增加了多普勒滤波器组输出端的虚警概率。减小谱旁瓣影响的一种方法是利用窗函数。然而,这种方法会导致主谱瓣变宽,降低多普勒分辨率。同时也增加了系统硬件的复杂性。在本工作中,将Bartlett方法用于频谱估计,该方法依赖于将接收到的数据序列划分为非重叠段的K个数,并在K上平均每个段的计算FFT,而不是典型地基雷达的直接FFT。与传统方法相比,该方法在存在地面和天气杂波的情况下,通过提供更高的检测概率、更低的虚警率和7-10 dB的额外增益,增强了目标检测能力。这是因为获得的旁瓣电平的幅度非常小。这反过来又有利于实现多普勒滤波器组而不使用额外的加权。采用带加权函数的直接FFT对接收到的总数据序列进行处理,可以达到所获得的性能,但与所提出的方法相比,这种方法的硬件复杂度更高,计算时间更长。计算机仿真结果证明了该方法的优越性。*埃及武装部队第11届ASA7会议论文集,2005年5月17-19日RR-03 910
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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