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Multiple radar targets estimation by exploiting induced amplitude modulation

机译:利用诱导幅度调制,多雷达目标估计

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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:这项工作涉及估计在监视雷达的相同范围 - 方位解析单元中存在的多个目标的复杂幅度,多普勒频率和到达的到达方向(DOA)的问题。推导出最大可能性(ML)和渐变(大样本大小)mL(AML)估计。为了降低AML估计器的非线性二维标准功能的最大化的计算复杂性,我们提出了一种基于弛豫方法的计算高效算法。它允许将信号分量的参数联合估计到更简单问题的序列中的问题,其中分别和迭代地估计每个分量的参数。所提出的方法克服了经典单调技术的分辨率限制,并且尽可能长地解析了表现出各方位角的任意较小差异的多个目标,只要它们的多普勒频率至少通过集成脉冲的数量相反,就提供了足够的信号 - 噪声每个脉冲的比率(SNR)可用。通过Monte Carlo模拟和Cramer-Rao下限(CRLB)计算,数值研究了所提出的AML-Relax估计器的性能。

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