距离角度失配条件下的FDA-MIMO雷达运动目标检测算法
Moving Target Detection Algorithm for FDA-MIMO Radar Under Range Angle Mismatch Conditions
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摘要: 频控阵-多输入多输出(Frequency Diverse Array-Multiple Input Multiple Output, FDA-MIMO)雷达是一种新体制雷达,其发射频率分集特性带来了额外的距离维信息,然而采样误差同样带来了导向矢量失配的问题,不仅如此,角度误差的存在也会进一步加重导向矢量的失配,极大地影响检测器的检测性能。此外,目标速度过快也会对FDA-MIMO雷达的目标检测性能产生影响。速度带来的影响具体表现在两个方面:一方面会导致目标的距离走动,从而导致不同慢时间的回波包络不能对齐,无法相干积累;二是频率增量引起的多普勒扩展,使得不同发射通道的多普勒频率不一样,这会进一步影响检测性能。针对上述问题,本文针对运动目标情况下的目标检测问题进行研究,为了解决目标运动带来的距离徙动和多普勒扩展效应,引入Keystone变换进行校正。此外,为了提升阵列失配条件下的目标检测性能,本文引入子空间模型,提出了距离角度失配情况下的子空间构建方法,并基于广义似然比检验(Generalized Likelihood Ratio Test,GLRT)准则推导了FDA-MIMO雷达在距离和角度失配条件下的自适应检测器。仿真结果表明:在高斯白噪声背景下,所提算法可以校正运动目标在速度较快情况下导致的距离徙动和多普勒扩展效应,且在阵列距离和角度失配条件下的检测性能优于传统的GLRT检测器。此外本文所提 Keystone -空域处理检测器与 Keystone -全空时处理检测器的性能接近,且计算复杂度更低。Abstract: By employing a frequency increment across the array antenna, Frequency Diverse Array-Multiple Input Multiple Output (FDA-MIMO) radar is able to produce a range-direction dependent response, which is different from conventional phased array radar that can only generate direction dependent beampattern. This unique feature brings about additional degree of freedom in range dimension. However, sampling error also brings about the problem of steering vector mismatch. Moreover, the presence of angle error will further exacerbate the mismatch of guidance vectors, greatly affecting the detection probability of the detector. In addition, excessive target speed can also have an impact on the target detection of FDA-MIMO radar. The impact of target velocity is manifested in two aspects: firstly, it can lead to range migration and Doppler frequency migration of radar echo signals, which can cause diffusion of the echo signal in both the range and Doppler dimensions, reducing the accumulated gain of the radar, and thus affecting the detection performance of the moving targets; secondly, it can cause phase changes in the radar echo signal, which can affect the parameter estimation accuracy for the targets; thirdly, the Doppler frequency expansion caused by frequency increment may occur, which further affects radar detection performance. The objective of this paper is to deal with the above issues. This article focuses on the problem of target detection in moving target in Gaussian noise background. In order to address the range migration and Doppler spread caused by target motion, a new algorithm is proposed in this paper, which is based on a modified Keystone transform. The echo signal model of the FDA-MIMO radar for moving target is derived first. On this basis, in moving target detection, the range migration and Doppler expansion caused by the frequency increment of FDA-MIMO radar is analyzed. To solve this problem, we develop a modified Keystone transform algorithm, where the frequency offset across the transmit array is taken into consideration. This method is not going to need a parameter searching process, which reduces the system computational complexity and improves the efficiency for real-time signal processing. After the problems of range migration and Doppler frequency expansion are solved, the array mismatch still exists. In addition, in order to improve the target detection performance under array mismatch conditions, this paper derives an adaptive detector under range and angle mismatch. The idea of signal subspace is introduced into this problem, where we can separate the target signal and array mismatches in different subspace. On this basis this paper proposes a subspace construction method for FDA-MIMO radar under range and angle mismatch conditions. Furthermore, we resort to the GLRT (Generalized Likelihood Ratio Test) criterion, and derive an adaptive detector for FDA-MIMO radar under range and angle mismatch conditions. The simulation results show that in the background of Gaussian white noise, the proposed algorithm can correct range migration and Doppler spread effects caused by target velocity, and its detection performance is superior to traditional GLRT methods under array range angle mismatch conditions. In addition, the performance of the Keystone spatial processing detector proposed in this article is similar to that of the Keystone all spatial processing detector, but with lower computational complexity.