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Multiple radar targets estimation by exploiting induced amplitude modulation

机译:利用感应幅度调制估计多个雷达目标

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This work deals with the problem of estimating complex amplitudes, Doppler frequencies, and directions of arrival (DOA) of multiple targets present in the same range-azimuth resolution cell of a surveillance radar. The maximum likelihood (ML) and the asymptotic (large sample size) ML (AML) estimators are derived. To reduce the computational complexity of the maximization of the nonlinear two-dimensional criterion function of the AML estimator, we propose a computationally efficient algorithm based on the RELAXation method. It allows decoupling the problem of jointly estimating the parameters of the signal components into a sequence of simpler problems, where the parameters of each component are separately and iteratively estimated. The proposed method overcomes the resolution limitation of the classical monopulse technique and resolves multiple targets exhibiting an arbitrarily small difference in azimuth as long as their Doppler frequencies differ at least by the inverse of the number of integrated pulses, provided that enough signal-to-noise ratio (SNR) per pulse is available. The performance of the proposed AML-RELAX estimator is numerically investigated through Monte Carlo simulation and Cramer-Rao lower bound (CRLB) calculation.
机译:这项工作涉及估算监视雷达的同一距离方位角分辨单元中存在的多个目标的复振幅,多普勒频率和到达方向(DOA)的问题。得出最大似然(ML)和渐近(大样本量)ML(AML)估计量。为了降低AML估计器的非线性二维准则函数最大化的计算复杂度,我们提出了一种基于RELAXation方法的高效计算算法。它允许将联合估计信号分量的参数的问题解耦为一系列简单问题,其中分别对各个分量的参数进行迭代估计。所提出的方法克服了传统单脉冲技术的分辨率限制,并解决了多个目标,只要它们的多普勒频率至少相差积分脉冲数的倒数,只要它们具有足够的信噪比,它们就可以显示出任意小的方位角差异每个脉冲的比率(SNR)可用。通过蒙特卡洛模拟和Cramer-Rao下界(CRLB)计算,对提出的AML-RELAX估计器的性能进行了数值研究。

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